Introduction

For one of my machine learning classes we had a project that consumed financial data. I have extended that project to use machine learning to see if an indicator, or predictor, can be found that identifies market tops that occur prior to recessions. Then I use the model to build a trading strategy and backtest it to see how it performs.

Get Economic and Financial Data

Acquiring the data consists of two steps. First the code pulls the data into zoo objects which are then collapsed into a single data frame (df.data). Features are extracted from these series and added to the df.data data frame.

Sample call to pull economic data

Data is pulled from several sources include FRED, yahoo, and Google. The code below shows an example that pulls in the consumer price index (CPI) from the FRED. I pull data using quantmod, Quandl, and some manual extractions stored in spreadsheets.

# Consumer Price Index for All Urban Consumers: All Items
if (bRefresh == TRUE) {
  getSymbols("CPIAUCSL", src = "FRED", auto.assign = TRUE)
}
## [1] "CPIAUCSL"
## Warning: ASDAX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.
## Warning: ^TNX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.
## Warning: CL=F contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.
## Warning: ^IRX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.
## Warning: ^STOXX50E contains missing values. Some functions will not work if
## objects contain missing values in the middle of the series. Consider using
## na.omit(), na.approx(), na.fill(), etc to remove or replace them.

Load up the EIA data

## Warning in .getMonEIA(ID, key = key): NAs introduced by coercion

## Warning in .getMonEIA(ID, key = key): NAs introduced by coercion

Load rig count data

The Baker Hughes rig count numbers

USDA data

Loading in farm data

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
## Expecting numeric in E3 / R3C5: got a date
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * ...
## Warning: NAs introduced by coercion

Loading in Silverblatt’s S&P 500 spreadsheet starting with the quarterly data.

## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...5
## * `` -> ...6
## * `` -> ...7

Now load in the estimates

## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * ...

Covid 19 Data

Get the Covid-19 data from JHU

## Parsed with column specification:
## cols(
##   date = col_date(format = ""),
##   province = col_character(),
##   country = col_character(),
##   lat = col_double(),
##   long = col_double(),
##   type = col_character(),
##   cases = col_double()
## )
## Downloading GitHub repo RamiKrispin/coronavirus@master
##   
  
  
v  checking for file 'C:\Users\Rainy\AppData\Local\Temp\RtmpgPVUV1\remotes711040ad27c6\RamiKrispin-coronavirus-aced7f2/DESCRIPTION'
## 
  
  
  
-  preparing 'coronavirus': (579ms)
##    checking DESCRIPTION meta-information ...
  
   checking DESCRIPTION meta-information ... 
  
v  checking DESCRIPTION meta-information
## 
  
  
  
-  checking for LF line-endings in source and make files and shell scripts
## 
  
  
  
-  checking for empty or unneeded directories
## 
  
  
  
-  looking to see if a 'data/datalist' file should be added
## 
  
  
  
-  building 'coronavirus_0.3.3.tar.gz'
## 
  
   
## 
## Installing package into 'C:/Users/Rainy/Documents/R/win-library/3.6'
## (as 'lib' is unspecified)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` regrouping output by 'country' (override with `.groups` argument)

## Warning: The `i` argument of ``[.tbl_df`()` must lie in [0, rows] if positive, as of tibble 3.0.0.
## Use `NA` as row index to obtain a row full of `NA` values.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Warning: Removed 2 row(s) containing missing values (geom_path).

Feature Extraction

With the raw data downloaded, some of the interesting features can be extracted. The first step is reconcile the time intervals. Some of the data is released monthly and some daily. I chose to interpolate all data to a daily interval. The first section of code adds the daily rows to the dataframe.

The code performs interpolation for continuous data or carries it forward for binary data like the recession indicators.

source("calcInterpolate.r")
df.data <- calcInterpolate(df.data, df.symbols)
## Warning in merge.xts(xtsData, get(df.symbols$string.symbol[idx])): NAs
## introduced by coercion

Truncate data

Create aggregate series

Some analysis requires that two or more series be combined. For example, normallizing debt by GDP to get a sense of the proportion of debt to the total economy helps understand the debt cycle.

Year over year, smoothed derivative, and log trends tend to smooth out seasonal variation. It gets used so often that I do this for every series downloaded.

source("calcFeatures.r")
lst.df <- calcFeatures(df.data, df.symbols)
## [1] "USREC has zero or negative values. Log series will be zero."
## [1] "GSFTX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMIX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMCX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMAX.Volume has zero or negative values. Log series will be zero."
## [1] "LCSIX.Volume has zero or negative values. Log series will be zero."
## [1] "VBIRX.Volume has zero or negative values. Log series will be zero."
## [1] "VFSUX.Volume has zero or negative values. Log series will be zero."
## [1] "LTUIX.Volume has zero or negative values. Log series will be zero."
## [1] "PTTPX.Volume has zero or negative values. Log series will be zero."
## [1] "NERYX.Volume has zero or negative values. Log series will be zero."
## [1] "STIGX.Volume has zero or negative values. Log series will be zero."
## [1] "HLGAX.Volume has zero or negative values. Log series will be zero."
## [1] "FTRGX.Volume has zero or negative values. Log series will be zero."
## [1] "THIIX.Volume has zero or negative values. Log series will be zero."
## [1] "PTTRX.Volume has zero or negative values. Log series will be zero."
## [1] "BFIGX.Volume has zero or negative values. Log series will be zero."
## [1] "EIFAX.Volume has zero or negative values. Log series will be zero."
## [1] "ASDAX.Volume has zero or negative values. Log series will be zero."
## [1] "TRBUX.Volume has zero or negative values. Log series will be zero."
## [1] "PRWCX.Volume has zero or negative values. Log series will be zero."
## [1] "ADOZX.Volume has zero or negative values. Log series will be zero."
## [1] "MERFX.Volume has zero or negative values. Log series will be zero."
## [1] "CMNIX.Volume has zero or negative values. Log series will be zero."
## [1] "CIHEX.Volume has zero or negative values. Log series will be zero."
## [1] "SRPSABSNNCB has zero or negative values. Log series will be zero."
## [1] "TNX.Volume has zero or negative values. Log series will be zero."
## [1] "CLF.Open has zero or negative values. Log series will be zero."
## [1] "CLF.Low has zero or negative values. Log series will be zero."
## [1] "CLF.Close has zero or negative values. Log series will be zero."
## [1] "CLF.Volume has zero or negative values. Log series will be zero."
## [1] "CLF.Adjusted has zero or negative values. Log series will be zero."
## [1] "DTB3 has zero or negative values. Log series will be zero."
## [1] "IRX.Open has zero or negative values. Log series will be zero."
## [1] "IRX.High has zero or negative values. Log series will be zero."
## [1] "IRX.Low has zero or negative values. Log series will be zero."
## [1] "IRX.Close has zero or negative values. Log series will be zero."
## [1] "IRX.Volume has zero or negative values. Log series will be zero."
## [1] "IRX.Adjusted has zero or negative values. Log series will be zero."
## [1] "DCOILWTICO has zero or negative values. Log series will be zero."
## [1] "RLG.Volume has zero or negative values. Log series will be zero."
## [1] "STOXX50E.Volume has zero or negative values. Log series will be zero."
## [1] "GDPNOW has zero or negative values. Log series will be zero."
## [1] "W790RC1Q027SBEA has zero or negative values. Log series will be zero."
## [1] "VXX.Volume has zero or negative values. Log series will be zero."
## [1] "FYFSD has zero or negative values. Log series will be zero."
## [1] "FYFSGDA188S has zero or negative values. Log series will be zero."
## [1] "SOFR25 has zero or negative values. Log series will be zero."
## [1] "SOFR1 has zero or negative values. Log series will be zero."
## [1] "RPONTSYD has zero or negative values. Log series will be zero."
## [1] "BOPGTB has zero or negative values. Log series will be zero."
## [1] "EES.Volume has zero or negative values. Log series will be zero."
## [1] "VGSTX.Volume has zero or negative values. Log series will be zero."
## [1] "VFINX.Volume has zero or negative values. Log series will be zero."
## [1] "TMFGX.Volume has zero or negative values. Log series will be zero."
## [1] "HAINX.Volume has zero or negative values. Log series will be zero."
## [1] "IVOO.Volume has zero or negative values. Log series will be zero."
## [1] "VO.Volume has zero or negative values. Log series will be zero."
## [1] "CZA.Volume has zero or negative values. Log series will be zero."
## [1] "SLY.Volume has zero or negative values. Log series will be zero."
## [1] "HYMB.Volume has zero or negative values. Log series will be zero."
## [1] "GOLD.Open has zero or negative values. Log series will be zero."
## [1] "GOLD.Volume has zero or negative values. Log series will be zero."
## [1] "BKR.Open has zero or negative values. Log series will be zero."
## [1] "BKR.Volume has zero or negative values. Log series will be zero."
## [1] "HAL.Open has zero or negative values. Log series will be zero."
## [1] "HAL.Volume has zero or negative values. Log series will be zero."
## [1] "IP.Open has zero or negative values. Log series will be zero."
## [1] "T.Open has zero or negative values. Log series will be zero."
## [1] "OPEARNINGSPERSHARE has zero or negative values. Log series will be zero."
## [1] "AREARNINGSPERSHARE has zero or negative values. Log series will be zero."
## [1] "OCCEquityVolume has zero or negative values. Log series will be zero."
## [1] "OCCNonEquityVolume has zero or negative values. Log series will be zero."
## [1] "BUSLOANS.minus.BUSLOANSNSA has zero or negative values. Log series will be zero."
## [1] "BUSLOANS.minus.BUSLOANSNSA.by.GDP has zero or negative values. Log series will be zero."
## [1] "EXPCH.minus.IMPCH has zero or negative values. Log series will be zero."
## [1] "EXPMX.minus.IMPMX has zero or negative values. Log series will be zero."
## [1] "SRPSABSNNCB.by.GDP has zero or negative values. Log series will be zero."
## [1] "DGS30TO10 has zero or negative values. Log series will be zero."
## [1] "DGS10TO1 has zero or negative values. Log series will be zero."
## [1] "DGS10TO2 has zero or negative values. Log series will be zero."
## [1] "DGS10TOTB3MS has zero or negative values. Log series will be zero."
## [1] "DGS10TODTB3 has zero or negative values. Log series will be zero."
## [1] "DCOILWTICO.by.PPIACO has zero or negative values. Log series will be zero."
## [1] "GSPC.DailySwing has zero or negative values. Log series will be zero."
df.data <- lst.df[[1]]
df.symbols <- lst.df[[2]]

Recession calculations

Summary calculations

These values are used below

Conclusion

In this worksheet a model predicting the onset of recession was built. From the model a trading rule was derived to allow backtesting. The model performed well and the trading rule backtesting showed that applying this in the post-WWII period would have resulted in an increase in returns. That is not too bad, but there are a few changes that would likely improve the model:

Market Conditions

#The model is predicting a `r paste(sprintf("%3.0f", tail(df.data$recession.initiation.smooth.avg,1)[[1]]*100), "%", sep="")` chance of recession in the next 12 months. :

#- P/E ratio of `r sprintf("%3.2f", tail(df.data$MULTPLSP500PERATIOMONTH,1))` compares to a historical mean value over the last decade of `r sprintf("%3.2f", df.data$MULTPLSP500PERATIOMONTH_Mean[1])`. Since 2008 recession P/E has only fallen below historical norm a few times. The current value is high, but well off the peaks. If earnings are +2-4% year-over-year then it is not unrealistic.

As of Feb 2020 we have entered a recession as defined by the NBER yet the market continues to rise.

P/E ratio of 35.29 compares to a historical mean value over the last decade of 18.57. Since 2008 recession P/E has only fallen below historical norm a few times. The current value is high, but well off the peaks. If earnings are +2-4% year-over-year then it is not unrealistic.

  • S&P 500 Volume, last updated on 2021-09-09, is negative over the last year and positive over the last month.

Unemployment

  • Headline unemployment (U-3) stands at 5.20% (last updated on 2021-08-01) which is near the 1-year average of 6.08% and rising with respect to the low in the last twelve months of 5.20%. Unlikely the rate will drop again.

  • Payrolls (BLS data, NSA) year-over-year stands at 3.14% which is above the 1-year average of 0.21% and falling with respect to the peak, in the last twelve months, of 10.86%.

  • Jobless claims (ICSA data) year-over-year stands at -64.33% (last updated on 2021-09-04) which is in-line with the 1-year average of 102.35% and below the peak, in the last twelve months, of 337.62%.
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_hline).

Personal Income

  • Real personal income year over year growth stands at 1.36% (last updated on 2021-07-01). This is below the recent peak of 7.97%.

Yield Curve and Bond Market

  • The 10-year to 3-month yield stands at 1.30% (last updated on 2021-09-08). This is above the recent low of 0.56%. The trend is positive over the last year and positive over the last month.

  • Auto sales flat?

Auxillary Series

I explored additional data series. The sections below have those data series along with comments.

Recent Highs

Print out the new 180 day high values

df.symbolsTrue <-
  df.symbols[df.symbols$'Max180' == TRUE, c("string.symbol", "string.description")]
df.symbolsTrue <-
  df.symbolsTrue[!(is.na(df.symbolsTrue$string.symbol)), ]
df.symbolsTrue <-
  df.symbolsTrue[!(df.symbolsTrue$string.symbol == 'USREC'), ]
#print(head(df.symbolsTrue,20))

kable(df.symbolsTrue, caption = "6-Month High") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))  
6-Month High
string.symbol string.description
1 CPIAUCSL Consumer Price Index for All Urban Consumers: All Items
4 PCEPI Personal Consumption Expenditures: Chain-type Price Index
7 NPPTTL Total Nonfarm Private Payroll Employment (ADP)
9 PAYNSA All Employees: Total Nonfarm Payrolls (NSA)
10 TABSHNO Households and nonprofit organizations; total assets, Level
11 HNONWPDPI Household Net Worth, percent Dispsable Income
12 INDPRO Industrial Production Index
14 RSALES Real Retail Sales (DISCONTINUED)
15 W875RX1 Real personal income excluding current transfer receipts
49 IMPCH U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
54 HNFSUSNSA New One Family Houses for Sale in the United States (Monthly, NSA)
58 REALLNNSA Real Estate Loans, All Commercial Banks (Monthly, NSA)
59 REALLN Real Estate Loans, All Commercial Banks (Monthly, SA)
61 RELACBW027SBOG Real Estate Loans, All Commercial Banks (Weekly, SA)
62 RREACBM027NBOG Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
64 RREACBW027SBOG Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
67 CONSUMERNSA Consumer Loans, All Commercial Banks
69 DPSACBW027SBOG Deposits, All Commercial Banks
72 SRPSABSNNCB Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
73 ASTLL All sectors; total loans; liability, Level (NSA)
74 FBDILNECA Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
75 ASOLAL All sectors; other loans and advances; liability, Level (NSA)
76 ASTMA All sectors; total mortgages; asset, Level (NSA)
77 ASHMA All sectors; home mortgages; asset, Level (NSA)
78 ASMRMA All sectors; multifamily residential mortgages; asset, Level (NSA)
79 ASCMA All sectors; commercial mortgages; asset, Level (NSA)
80 ASFMA All sectors; farm mortgages; asset, Level (NSA)
81 CCLBSHNO Households and nonprofit organizations; consumer credit; liability, Level (NSA)
82 FBDSILQ027S Domestic financial sectors debt securities; liability, Level (NSA)
83 FBLL Domestic financial sectors loans; liability, Level (NSA)
84 NCBDBIQ027S Nonfinancial corporate business; debt securities; liability, Level
91 TB3MS 3-Month Treasury Bill: Secondary Market Rate (Monthly)
96 NEWORDER Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft
104 GDP Gross Domestic Product
106 FDEFX Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
108 GDPC1 Real Gross Domestic Product
109 GDPDEF Gross Domestic Product: Implicit Price Deflator
111 WLRRAL Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
115 MZMV Velocity of MZM Money Stock
116 M1 M1 Money Stock
117 M2 M2 Money Stock
118 OPHNFB Nonfarm Business Sector: Real Output Per Hour of All Persons
119 IPMAN Industrial Production: Manufacturing (NAICS)
126 GFDEBTN Federal Debt: Total Public Debt
128 MSPUS Median Sales Price of Houses Sold for the United States
131 CSUSHPINSA S&P/Case-Shiller U.S. National Home Price Index (NSA)
133 FYFSD Federal Surplus or Deficit
134 FYFSGDA188S Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
139 WALCL All Federal Reserve Banks: Total Assets
140 OUTMS Manufacturing Sector: Real Output
141 MANEMP All Employees: Manufacturing
142 PRS30006163 Manufacturing Sector: Real Output Per Person
145 SOFR Secured Overnight Financing Rate
147 SOFR99 Secured Overnight Financing Rate: 99th Percentile
149 SOFR25 Secured Overnight Financing Rate: 25th Percentile
157 IOER Interest Rate on Excess Reserves
158 WRESBAL Reserve Balances with Federal Reserve Banks
159 EXCSRESNW Excess Reserves of Depository Institutions
160 ECBASSETS Central Bank Assets for Euro Area (11-19 Countries)
161 EUNNGDP Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
162 CEU0600000007 Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
164 CURRENCY Currency Component of M1 (Seasonally Adjusted)
165 WCURRNS Currency Component of M1
166 BOGMBASE Monetary Base; Total
167 PRS88003193 Nonfinancial Corporations Sector: Unit Profits
168 PPIACO Producer Price Index for All Commodities
169 PCUOMFGOMFG Producer Price Index by Industry: Total Manufacturing Industries
170 POPTHM Population (U.S.)
171 POPTHM Population (U.S.)
172 CLF16OV Civilian Labor Force Level, SA
179 TERMCBPER24NS Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan
180 A065RC1A027NBEA Personal income (NSA)
182 PCE Personal Consumption Expenditures (SA)
183 A053RC1Q027SBEA National income: Corporate profits before tax (without IVA and CCAdj)
184 CPROFIT Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj)
219 MULTPLSP500SALESQUARTER S&P 500 TTM Sales (Not Inflation Adjusted)
223 WWDIWLDISAIRGOODMTK1 Air transport, freight
226 PETA143B00001M U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly
228 TOTALOGNRPUSM Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly
229 TOTALPANRPUSM Crude Oil Rotary Rigs in Operation, Monthly
230 TOTALNGNRPUSM Natural Gas Rotary Rigs in Operation, Monthly
231 BKRTotal Total Rig Count
232 BKRGas Gas Rig Count
233 BKROil Oil Rig Count
234 FARMINCOME Net Farm Income
235 OPEARNINGSPERSHARE Operating Earnings per Share
236 AREARNINGSPERSHARE As-Reported Earnings per Share
237 CASHDIVIDENDSPERSHR Cash Dividends per Share
239 BOOKVALPERSHR Book value per Share
241 PRICE Price
242 OPEARNINGSTTM TTM Operating Earnings
243 AREARNINGSTTM TTM Reported Earnings
246 OCCEquityVolume Equity Options Volume
247 OCCNonEquityVolume Non-Equity Options Volume
249 BUSLOANS.minus.BUSLOANSNSA Business Loans (Montlhy) SA - NSA
250 BUSLOANS.minus.BUSLOANSNSA.by.GDP Business Loans (Montlhy) SA - NSA divided by GDP
262 A053RC1Q027SBEA.by.GDP National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP
263 CPROFIT.by.GDP National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP
264 CONSUMERNSA.by.GDP Consumer Loans Not Seasonally Adjusted divided by GDP
274 CONSUMERNSA.INTEREST Consumer Loans (Not Seasonally Adjusted) Interest Burdens
275 CONSUMERNSA.INTEREST.by.GDP Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
280 WRESBAL.by.GDP Reserve Balances with Federal Reserve Banks Divided by GDP
282 WLRRAL.by.GDP Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
289 ASFMA.by.ASTLL All sectors; total loans Divided by farm mortgages
293 BOGMBASE.by.GDP BOGMBASE Divided by GDP
294 WALCL.by.GDP All Federal Reserve Banks: Total Assets Divided by GDP
304 NPPTTLBYPOPTHM ADP Private Employment / Population
323 HNFSUSNSA.minus.HSN1FNSA Houses for sale - houses sold
325 MSPUS.times.HNFSUSNSA New privately owned 1-family units for sale times median price
331 CPIAUCSL_SmoothDer Derivative of Smoothed Consumer Price Index for All Urban Consumers: All Items
332 CPIAUCSL_Log Log of Consumer Price Index for All Urban Consumers: All Items
333 CPIAUCSL_mva200 Consumer Price Index for All Urban Consumers: All Items 200 Day MA
334 CPIAUCSL_mva050 Consumer Price Index for All Urban Consumers: All Items 50 Day MA
335 USREC_YoY NBER based Recession Indicators Year over Year
336 USREC_YoY4 NBER based Recession Indicators 4 Year over 4 Year
337 USREC_YoY5 NBER based Recession Indicators 5 Year over 5 Year
338 USREC_Smooth Savitsky-Golay Smoothed (p=3, n=365) NBER based Recession Indicators
339 USREC_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) NBER based Recession Indicators
341 USREC_Log Log of NBER based Recession Indicators
342 USREC_mva200 NBER based Recession Indicators 200 Day MA
343 USREC_mva050 NBER based Recession Indicators 50 Day MA
359 PCEPI_Log Log of Personal Consumption Expenditures: Chain-type Price Index
360 PCEPI_mva200 Personal Consumption Expenditures: Chain-type Price Index 200 Day MA
361 PCEPI_mva050 Personal Consumption Expenditures: Chain-type Price Index 50 Day MA
367 CCSA_SmoothDer Derivative of Smoothed Continued Claims (Insured Unemployment)
376 CCNSA_SmoothDer Derivative of Smoothed Continued Claims (Insured Unemployment, NSA)
383 NPPTTL_Smooth Savitsky-Golay Smoothed (p=3, n=365) Total Nonfarm Private Payroll Employment (ADP)
385 NPPTTL_SmoothDer Derivative of Smoothed Total Nonfarm Private Payroll Employment (ADP)
386 NPPTTL_Log Log of Total Nonfarm Private Payroll Employment (ADP)
387 NPPTTL_mva200 Total Nonfarm Private Payroll Employment (ADP) 200 Day MA
388 NPPTTL_mva050 Total Nonfarm Private Payroll Employment (ADP) 50 Day MA
403 PAYNSA_SmoothDer Derivative of Smoothed All Employees: Total Nonfarm Payrolls (NSA)
404 PAYNSA_Log Log of All Employees: Total Nonfarm Payrolls (NSA)
405 PAYNSA_mva200 All Employees: Total Nonfarm Payrolls (NSA) 200 Day MA
406 PAYNSA_mva050 All Employees: Total Nonfarm Payrolls (NSA) 50 Day MA
410 TABSHNO_Smooth Savitsky-Golay Smoothed (p=3, n=365) Households and nonprofit organizations; total assets, Level
413 TABSHNO_Log Log of Households and nonprofit organizations; total assets, Level
414 TABSHNO_mva200 Households and nonprofit organizations; total assets, Level 200 Day MA
415 TABSHNO_mva050 Households and nonprofit organizations; total assets, Level 50 Day MA
421 HNONWPDPI_SmoothDer Derivative of Smoothed Household Net Worth, percent Dispsable Income
422 HNONWPDPI_Log Log of Household Net Worth, percent Dispsable Income
424 HNONWPDPI_mva050 Household Net Worth, percent Dispsable Income 50 Day MA
428 INDPRO_Smooth Savitsky-Golay Smoothed (p=3, n=365) Industrial Production Index
430 INDPRO_SmoothDer Derivative of Smoothed Industrial Production Index
431 INDPRO_Log Log of Industrial Production Index
432 INDPRO_mva200 Industrial Production Index 200 Day MA
433 INDPRO_mva050 Industrial Production Index 50 Day MA
443 RSALES_YoY Real Retail Sales (DISCONTINUED) Year over Year
444 RSALES_YoY4 Real Retail Sales (DISCONTINUED) 4 Year over 4 Year
445 RSALES_YoY5 Real Retail Sales (DISCONTINUED) 5 Year over 5 Year
449 RSALES_Log Log of Real Retail Sales (DISCONTINUED)
450 RSALES_mva200 Real Retail Sales (DISCONTINUED) 200 Day MA
451 RSALES_mva050 Real Retail Sales (DISCONTINUED) 50 Day MA
455 W875RX1_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real personal income excluding current transfer receipts
457 W875RX1_SmoothDer Derivative of Smoothed Real personal income excluding current transfer receipts
458 W875RX1_Log Log of Real personal income excluding current transfer receipts
459 W875RX1_mva200 Real personal income excluding current transfer receipts 200 Day MA
460 W875RX1_mva050 Real personal income excluding current transfer receipts 50 Day MA
477 PCOPPUSDM_mva200 Global price of Copper 200 Day MA
486 NOBL.Open_mva200 200 Day MA
487 NOBL.Open_mva050 50 Day MA
495 NOBL.High_mva200 200 Day MA
496 NOBL.High_mva050 50 Day MA
504 NOBL.Low_mva200 200 Day MA
505 NOBL.Low_mva050 50 Day MA
513 NOBL.Close_mva200 200 Day MA
514 NOBL.Close_mva050 50 Day MA
531 NOBL.Adjusted_mva200 200 Day MA
532 NOBL.Adjusted_mva050 50 Day MA
540 SCHD.Open_mva200 200 Day MA
541 SCHD.Open_mva050 50 Day MA
549 SCHD.High_mva200 200 Day MA
550 SCHD.High_mva050 50 Day MA
558 SCHD.Low_mva200 200 Day MA
559 SCHD.Low_mva050 50 Day MA
567 SCHD.Close_mva200 200 Day MA
568 SCHD.Close_mva050 50 Day MA
585 SCHD.Adjusted_mva200 200 Day MA
586 SCHD.Adjusted_mva050 50 Day MA
590 PFF.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
592 PFF.Open_SmoothDer Derivative of Smoothed
594 PFF.Open_mva200 200 Day MA
595 PFF.Open_mva050 50 Day MA
599 PFF.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
601 PFF.High_SmoothDer Derivative of Smoothed
603 PFF.High_mva200 200 Day MA
604 PFF.High_mva050 50 Day MA
608 PFF.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
610 PFF.Low_SmoothDer Derivative of Smoothed
612 PFF.Low_mva200 200 Day MA
613 PFF.Low_mva050 50 Day MA
617 PFF.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
619 PFF.Close_SmoothDer Derivative of Smoothed
621 PFF.Close_mva200 200 Day MA
622 PFF.Close_mva050 50 Day MA
635 PFF.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
637 PFF.Adjusted_SmoothDer Derivative of Smoothed
639 PFF.Adjusted_mva200 200 Day MA
640 PFF.Adjusted_mva050 50 Day MA
646 HPI.Open_SmoothDer Derivative of Smoothed
648 HPI.Open_mva200 200 Day MA
649 HPI.Open_mva050 50 Day MA
655 HPI.High_SmoothDer Derivative of Smoothed
657 HPI.High_mva200 200 Day MA
658 HPI.High_mva050 50 Day MA
662 HPI.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
664 HPI.Low_SmoothDer Derivative of Smoothed
666 HPI.Low_mva200 200 Day MA
667 HPI.Low_mva050 50 Day MA
673 HPI.Close_SmoothDer Derivative of Smoothed
675 HPI.Close_mva200 200 Day MA
676 HPI.Close_mva050 50 Day MA
689 HPI.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
691 HPI.Adjusted_SmoothDer Derivative of Smoothed
693 HPI.Adjusted_mva200 200 Day MA
694 HPI.Adjusted_mva050 50 Day MA
698 GSFTX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
702 GSFTX.Open_mva200 200 Day MA
703 GSFTX.Open_mva050 50 Day MA
707 GSFTX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
711 GSFTX.High_mva200 200 Day MA
712 GSFTX.High_mva050 50 Day MA
716 GSFTX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
720 GSFTX.Low_mva200 200 Day MA
721 GSFTX.Low_mva050 50 Day MA
725 GSFTX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
729 GSFTX.Close_mva200 200 Day MA
730 GSFTX.Close_mva050 50 Day MA
731 GSFTX.Volume_YoY Year over Year
732 GSFTX.Volume_YoY4 4 Year over 4 Year
733 GSFTX.Volume_YoY5 5 Year over 5 Year
734 GSFTX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
735 GSFTX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
736 GSFTX.Volume_SmoothDer Derivative of Smoothed
737 GSFTX.Volume_Log Log of
738 GSFTX.Volume_mva200 200 Day MA
739 GSFTX.Volume_mva050 50 Day MA
743 GSFTX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
747 GSFTX.Adjusted_mva200 200 Day MA
748 GSFTX.Adjusted_mva050 50 Day MA
785 LFMIX.Volume_YoY Year over Year
786 LFMIX.Volume_YoY4 4 Year over 4 Year
787 LFMIX.Volume_YoY5 5 Year over 5 Year
788 LFMIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
789 LFMIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
790 LFMIX.Volume_SmoothDer Derivative of Smoothed
791 LFMIX.Volume_Log Log of
792 LFMIX.Volume_mva200 200 Day MA
793 LFMIX.Volume_mva050 50 Day MA
839 LFMCX.Volume_YoY Year over Year
840 LFMCX.Volume_YoY4 4 Year over 4 Year
841 LFMCX.Volume_YoY5 5 Year over 5 Year
842 LFMCX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
843 LFMCX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
844 LFMCX.Volume_SmoothDer Derivative of Smoothed
845 LFMCX.Volume_Log Log of
846 LFMCX.Volume_mva200 200 Day MA
847 LFMCX.Volume_mva050 50 Day MA
893 LFMAX.Volume_YoY Year over Year
894 LFMAX.Volume_YoY4 4 Year over 4 Year
895 LFMAX.Volume_YoY5 5 Year over 5 Year
896 LFMAX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
897 LFMAX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
898 LFMAX.Volume_SmoothDer Derivative of Smoothed
899 LFMAX.Volume_Log Log of
900 LFMAX.Volume_mva200 200 Day MA
901 LFMAX.Volume_mva050 50 Day MA
913 LCSIX.Open_YoY5 5 Year over 5 Year
916 LCSIX.Open_SmoothDer Derivative of Smoothed
917 LCSIX.Open_Log Log of
918 LCSIX.Open_mva200 200 Day MA
919 LCSIX.Open_mva050 50 Day MA
922 LCSIX.High_YoY5 5 Year over 5 Year
925 LCSIX.High_SmoothDer Derivative of Smoothed
926 LCSIX.High_Log Log of
927 LCSIX.High_mva200 200 Day MA
928 LCSIX.High_mva050 50 Day MA
931 LCSIX.Low_YoY5 5 Year over 5 Year
934 LCSIX.Low_SmoothDer Derivative of Smoothed
935 LCSIX.Low_Log Log of
936 LCSIX.Low_mva200 200 Day MA
937 LCSIX.Low_mva050 50 Day MA
940 LCSIX.Close_YoY5 5 Year over 5 Year
943 LCSIX.Close_SmoothDer Derivative of Smoothed
944 LCSIX.Close_Log Log of
945 LCSIX.Close_mva200 200 Day MA
946 LCSIX.Close_mva050 50 Day MA
947 LCSIX.Volume_YoY Year over Year
948 LCSIX.Volume_YoY4 4 Year over 4 Year
949 LCSIX.Volume_YoY5 5 Year over 5 Year
950 LCSIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
951 LCSIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
952 LCSIX.Volume_SmoothDer Derivative of Smoothed
953 LCSIX.Volume_Log Log of
954 LCSIX.Volume_mva200 200 Day MA
955 LCSIX.Volume_mva050 50 Day MA
958 LCSIX.Adjusted_YoY5 5 Year over 5 Year
962 LCSIX.Adjusted_Log Log of
963 LCSIX.Adjusted_mva200 200 Day MA
964 LCSIX.Adjusted_mva050 50 Day MA
970 BSV.Open_SmoothDer Derivative of Smoothed
979 BSV.High_SmoothDer Derivative of Smoothed
988 BSV.Low_SmoothDer Derivative of Smoothed
997 BSV.Close_SmoothDer Derivative of Smoothed
1004 BSV.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1013 BSV.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1015 BSV.Adjusted_SmoothDer Derivative of Smoothed
1024 VBIRX.Open_SmoothDer Derivative of Smoothed
1033 VBIRX.High_SmoothDer Derivative of Smoothed
1042 VBIRX.Low_SmoothDer Derivative of Smoothed
1051 VBIRX.Close_SmoothDer Derivative of Smoothed
1055 VBIRX.Volume_YoY Year over Year
1056 VBIRX.Volume_YoY4 4 Year over 4 Year
1057 VBIRX.Volume_YoY5 5 Year over 5 Year
1058 VBIRX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1059 VBIRX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1060 VBIRX.Volume_SmoothDer Derivative of Smoothed
1061 VBIRX.Volume_Log Log of
1062 VBIRX.Volume_mva200 200 Day MA
1063 VBIRX.Volume_mva050 50 Day MA
1067 VBIRX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1069 VBIRX.Adjusted_SmoothDer Derivative of Smoothed
1076 BIV.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1078 BIV.Open_SmoothDer Derivative of Smoothed
1085 BIV.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1087 BIV.High_SmoothDer Derivative of Smoothed
1094 BIV.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1096 BIV.Low_SmoothDer Derivative of Smoothed
1103 BIV.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1105 BIV.Close_SmoothDer Derivative of Smoothed
1121 BIV.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1123 BIV.Adjusted_SmoothDer Derivative of Smoothed
1132 VFSUX.Open_SmoothDer Derivative of Smoothed
1141 VFSUX.High_SmoothDer Derivative of Smoothed
1150 VFSUX.Low_SmoothDer Derivative of Smoothed
1159 VFSUX.Close_SmoothDer Derivative of Smoothed
1163 VFSUX.Volume_YoY Year over Year
1164 VFSUX.Volume_YoY4 4 Year over 4 Year
1165 VFSUX.Volume_YoY5 5 Year over 5 Year
1166 VFSUX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1167 VFSUX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1168 VFSUX.Volume_SmoothDer Derivative of Smoothed
1169 VFSUX.Volume_Log Log of
1170 VFSUX.Volume_mva200 200 Day MA
1171 VFSUX.Volume_mva050 50 Day MA
1175 VFSUX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1177 VFSUX.Adjusted_SmoothDer Derivative of Smoothed
1179 VFSUX.Adjusted_mva200 200 Day MA
1186 LTUIX.Open_SmoothDer Derivative of Smoothed
1195 LTUIX.High_SmoothDer Derivative of Smoothed
1204 LTUIX.Low_SmoothDer Derivative of Smoothed
1213 LTUIX.Close_SmoothDer Derivative of Smoothed
1217 LTUIX.Volume_YoY Year over Year
1218 LTUIX.Volume_YoY4 4 Year over 4 Year
1219 LTUIX.Volume_YoY5 5 Year over 5 Year
1220 LTUIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1221 LTUIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1222 LTUIX.Volume_SmoothDer Derivative of Smoothed
1223 LTUIX.Volume_Log Log of
1224 LTUIX.Volume_mva200 200 Day MA
1225 LTUIX.Volume_mva050 50 Day MA
1229 LTUIX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1231 LTUIX.Adjusted_SmoothDer Derivative of Smoothed
1238 PTTPX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1240 PTTPX.Open_SmoothDer Derivative of Smoothed
1247 PTTPX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1249 PTTPX.High_SmoothDer Derivative of Smoothed
1256 PTTPX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1258 PTTPX.Low_SmoothDer Derivative of Smoothed
1265 PTTPX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1267 PTTPX.Close_SmoothDer Derivative of Smoothed
1271 PTTPX.Volume_YoY Year over Year
1272 PTTPX.Volume_YoY4 4 Year over 4 Year
1273 PTTPX.Volume_YoY5 5 Year over 5 Year
1274 PTTPX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1275 PTTPX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1276 PTTPX.Volume_SmoothDer Derivative of Smoothed
1277 PTTPX.Volume_Log Log of
1278 PTTPX.Volume_mva200 200 Day MA
1279 PTTPX.Volume_mva050 50 Day MA
1283 PTTPX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1285 PTTPX.Adjusted_SmoothDer Derivative of Smoothed
1292 NERYX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1294 NERYX.Open_SmoothDer Derivative of Smoothed
1301 NERYX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1303 NERYX.High_SmoothDer Derivative of Smoothed
1310 NERYX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1312 NERYX.Low_SmoothDer Derivative of Smoothed
1319 NERYX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1321 NERYX.Close_SmoothDer Derivative of Smoothed
1325 NERYX.Volume_YoY Year over Year
1326 NERYX.Volume_YoY4 4 Year over 4 Year
1327 NERYX.Volume_YoY5 5 Year over 5 Year
1328 NERYX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1329 NERYX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1330 NERYX.Volume_SmoothDer Derivative of Smoothed
1331 NERYX.Volume_Log Log of
1332 NERYX.Volume_mva200 200 Day MA
1333 NERYX.Volume_mva050 50 Day MA
1337 NERYX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1339 NERYX.Adjusted_SmoothDer Derivative of Smoothed
1346 STIGX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1348 STIGX.Open_SmoothDer Derivative of Smoothed
1355 STIGX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1357 STIGX.High_SmoothDer Derivative of Smoothed
1364 STIGX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1366 STIGX.Low_SmoothDer Derivative of Smoothed
1373 STIGX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1375 STIGX.Close_SmoothDer Derivative of Smoothed
1379 STIGX.Volume_YoY Year over Year
1380 STIGX.Volume_YoY4 4 Year over 4 Year
1381 STIGX.Volume_YoY5 5 Year over 5 Year
1382 STIGX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1383 STIGX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1384 STIGX.Volume_SmoothDer Derivative of Smoothed
1385 STIGX.Volume_Log Log of
1386 STIGX.Volume_mva200 200 Day MA
1387 STIGX.Volume_mva050 50 Day MA
1391 STIGX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1393 STIGX.Adjusted_SmoothDer Derivative of Smoothed
1400 HLGAX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1402 HLGAX.Open_SmoothDer Derivative of Smoothed
1409 HLGAX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1411 HLGAX.High_SmoothDer Derivative of Smoothed
1418 HLGAX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1420 HLGAX.Low_SmoothDer Derivative of Smoothed
1427 HLGAX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1429 HLGAX.Close_SmoothDer Derivative of Smoothed
1433 HLGAX.Volume_YoY Year over Year
1434 HLGAX.Volume_YoY4 4 Year over 4 Year
1435 HLGAX.Volume_YoY5 5 Year over 5 Year
1436 HLGAX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1437 HLGAX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1438 HLGAX.Volume_SmoothDer Derivative of Smoothed
1439 HLGAX.Volume_Log Log of
1440 HLGAX.Volume_mva200 200 Day MA
1441 HLGAX.Volume_mva050 50 Day MA
1445 HLGAX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1447 HLGAX.Adjusted_SmoothDer Derivative of Smoothed
1454 FTRGX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1456 FTRGX.Open_SmoothDer Derivative of Smoothed
1463 FTRGX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1465 FTRGX.High_SmoothDer Derivative of Smoothed
1472 FTRGX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1474 FTRGX.Low_SmoothDer Derivative of Smoothed
1481 FTRGX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1483 FTRGX.Close_SmoothDer Derivative of Smoothed
1487 FTRGX.Volume_YoY Year over Year
1488 FTRGX.Volume_YoY4 4 Year over 4 Year
1489 FTRGX.Volume_YoY5 5 Year over 5 Year
1490 FTRGX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1491 FTRGX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1492 FTRGX.Volume_SmoothDer Derivative of Smoothed
1493 FTRGX.Volume_Log Log of
1494 FTRGX.Volume_mva200 200 Day MA
1495 FTRGX.Volume_mva050 50 Day MA
1499 FTRGX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1501 FTRGX.Adjusted_SmoothDer Derivative of Smoothed
1508 THIIX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1510 THIIX.Open_SmoothDer Derivative of Smoothed
1517 THIIX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1519 THIIX.High_SmoothDer Derivative of Smoothed
1526 THIIX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1528 THIIX.Low_SmoothDer Derivative of Smoothed
1535 THIIX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1537 THIIX.Close_SmoothDer Derivative of Smoothed
1541 THIIX.Volume_YoY Year over Year
1542 THIIX.Volume_YoY4 4 Year over 4 Year
1543 THIIX.Volume_YoY5 5 Year over 5 Year
1544 THIIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1545 THIIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1546 THIIX.Volume_SmoothDer Derivative of Smoothed
1547 THIIX.Volume_Log Log of
1548 THIIX.Volume_mva200 200 Day MA
1549 THIIX.Volume_mva050 50 Day MA
1553 THIIX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1555 THIIX.Adjusted_SmoothDer Derivative of Smoothed
1562 PTTRX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1564 PTTRX.Open_SmoothDer Derivative of Smoothed
1571 PTTRX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1573 PTTRX.High_SmoothDer Derivative of Smoothed
1580 PTTRX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1582 PTTRX.Low_SmoothDer Derivative of Smoothed
1589 PTTRX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1591 PTTRX.Close_SmoothDer Derivative of Smoothed
1595 PTTRX.Volume_YoY Year over Year
1596 PTTRX.Volume_YoY4 4 Year over 4 Year
1597 PTTRX.Volume_YoY5 5 Year over 5 Year
1598 PTTRX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1599 PTTRX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1600 PTTRX.Volume_SmoothDer Derivative of Smoothed
1601 PTTRX.Volume_Log Log of
1602 PTTRX.Volume_mva200 200 Day MA
1603 PTTRX.Volume_mva050 50 Day MA
1607 PTTRX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1609 PTTRX.Adjusted_SmoothDer Derivative of Smoothed
1615 BFIGX.Open_YoY5 5 Year over 5 Year
1616 BFIGX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1617 BFIGX.Open_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1618 BFIGX.Open_SmoothDer Derivative of Smoothed
1619 BFIGX.Open_Log Log of
1621 BFIGX.Open_mva050 50 Day MA
1624 BFIGX.High_YoY5 5 Year over 5 Year
1625 BFIGX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1626 BFIGX.High_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1627 BFIGX.High_SmoothDer Derivative of Smoothed
1628 BFIGX.High_Log Log of
1630 BFIGX.High_mva050 50 Day MA
1633 BFIGX.Low_YoY5 5 Year over 5 Year
1634 BFIGX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1635 BFIGX.Low_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1636 BFIGX.Low_SmoothDer Derivative of Smoothed
1637 BFIGX.Low_Log Log of
1639 BFIGX.Low_mva050 50 Day MA
1642 BFIGX.Close_YoY5 5 Year over 5 Year
1643 BFIGX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1644 BFIGX.Close_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1645 BFIGX.Close_SmoothDer Derivative of Smoothed
1646 BFIGX.Close_Log Log of
1648 BFIGX.Close_mva050 50 Day MA
1649 BFIGX.Volume_YoY Year over Year
1650 BFIGX.Volume_YoY4 4 Year over 4 Year
1651 BFIGX.Volume_YoY5 5 Year over 5 Year
1652 BFIGX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1653 BFIGX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1654 BFIGX.Volume_SmoothDer Derivative of Smoothed
1655 BFIGX.Volume_Log Log of
1656 BFIGX.Volume_mva200 200 Day MA
1657 BFIGX.Volume_mva050 50 Day MA
1660 BFIGX.Adjusted_YoY5 5 Year over 5 Year
1661 BFIGX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1662 BFIGX.Adjusted_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1663 BFIGX.Adjusted_SmoothDer Derivative of Smoothed
1664 BFIGX.Adjusted_Log Log of
1665 BFIGX.Adjusted_mva200 200 Day MA
1666 BFIGX.Adjusted_mva050 50 Day MA
1674 VTWO.Open_mva200 200 Day MA
1692 VTWO.Low_mva200 200 Day MA
1706 VTWO.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1719 VTWO.Adjusted_mva200 200 Day MA
1722 EIFAX.Open_YoY4 4 Year over 4 Year
1727 EIFAX.Open_Log Log of
1731 EIFAX.High_YoY4 4 Year over 4 Year
1736 EIFAX.High_Log Log of
1740 EIFAX.Low_YoY4 4 Year over 4 Year
1745 EIFAX.Low_Log Log of
1749 EIFAX.Close_YoY4 4 Year over 4 Year
1754 EIFAX.Close_Log Log of
1757 EIFAX.Volume_YoY Year over Year
1758 EIFAX.Volume_YoY4 4 Year over 4 Year
1759 EIFAX.Volume_YoY5 5 Year over 5 Year
1760 EIFAX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1761 EIFAX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1762 EIFAX.Volume_SmoothDer Derivative of Smoothed
1763 EIFAX.Volume_Log Log of
1764 EIFAX.Volume_mva200 200 Day MA
1765 EIFAX.Volume_mva050 50 Day MA
1769 EIFAX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1772 EIFAX.Adjusted_Log Log of
1773 EIFAX.Adjusted_mva200 200 Day MA
1780 ASDAX.Open_SmoothDer Derivative of Smoothed
1789 ASDAX.High_SmoothDer Derivative of Smoothed
1798 ASDAX.Low_SmoothDer Derivative of Smoothed
1807 ASDAX.Close_SmoothDer Derivative of Smoothed
1811 ASDAX.Volume_YoY Year over Year
1812 ASDAX.Volume_YoY4 4 Year over 4 Year
1813 ASDAX.Volume_YoY5 5 Year over 5 Year
1814 ASDAX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1815 ASDAX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1816 ASDAX.Volume_SmoothDer Derivative of Smoothed
1817 ASDAX.Volume_Log Log of
1818 ASDAX.Volume_mva200 200 Day MA
1819 ASDAX.Volume_mva050 50 Day MA
1825 ASDAX.Adjusted_SmoothDer Derivative of Smoothed
1827 ASDAX.Adjusted_mva200 200 Day MA
1834 TRBUX.Open_SmoothDer Derivative of Smoothed
1843 TRBUX.High_SmoothDer Derivative of Smoothed
1852 TRBUX.Low_SmoothDer Derivative of Smoothed
1861 TRBUX.Close_SmoothDer Derivative of Smoothed
1865 TRBUX.Volume_YoY Year over Year
1866 TRBUX.Volume_YoY4 4 Year over 4 Year
1867 TRBUX.Volume_YoY5 5 Year over 5 Year
1868 TRBUX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1869 TRBUX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1870 TRBUX.Volume_SmoothDer Derivative of Smoothed
1871 TRBUX.Volume_Log Log of
1872 TRBUX.Volume_mva200 200 Day MA
1873 TRBUX.Volume_mva050 50 Day MA
1879 TRBUX.Adjusted_SmoothDer Derivative of Smoothed
1886 PRWCX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1888 PRWCX.Open_SmoothDer Derivative of Smoothed
1890 PRWCX.Open_mva200 200 Day MA
1891 PRWCX.Open_mva050 50 Day MA
1895 PRWCX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1897 PRWCX.High_SmoothDer Derivative of Smoothed
1899 PRWCX.High_mva200 200 Day MA
1900 PRWCX.High_mva050 50 Day MA
1904 PRWCX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1906 PRWCX.Low_SmoothDer Derivative of Smoothed
1908 PRWCX.Low_mva200 200 Day MA
1909 PRWCX.Low_mva050 50 Day MA
1913 PRWCX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1915 PRWCX.Close_SmoothDer Derivative of Smoothed
1917 PRWCX.Close_mva200 200 Day MA
1918 PRWCX.Close_mva050 50 Day MA
1919 PRWCX.Volume_YoY Year over Year
1920 PRWCX.Volume_YoY4 4 Year over 4 Year
1921 PRWCX.Volume_YoY5 5 Year over 5 Year
1922 PRWCX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1923 PRWCX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1924 PRWCX.Volume_SmoothDer Derivative of Smoothed
1925 PRWCX.Volume_Log Log of
1926 PRWCX.Volume_mva200 200 Day MA
1927 PRWCX.Volume_mva050 50 Day MA
1931 PRWCX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1933 PRWCX.Adjusted_SmoothDer Derivative of Smoothed
1935 PRWCX.Adjusted_mva200 200 Day MA
1936 PRWCX.Adjusted_mva050 50 Day MA
1940 ADOZX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1943 ADOZX.Open_Log Log of
1944 ADOZX.Open_mva200 200 Day MA
1945 ADOZX.Open_mva050 50 Day MA
1949 ADOZX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1952 ADOZX.High_Log Log of
1953 ADOZX.High_mva200 200 Day MA
1954 ADOZX.High_mva050 50 Day MA
1958 ADOZX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1961 ADOZX.Low_Log Log of
1962 ADOZX.Low_mva200 200 Day MA
1963 ADOZX.Low_mva050 50 Day MA
1967 ADOZX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1970 ADOZX.Close_Log Log of
1971 ADOZX.Close_mva200 200 Day MA
1972 ADOZX.Close_mva050 50 Day MA
1973 ADOZX.Volume_YoY Year over Year
1974 ADOZX.Volume_YoY4 4 Year over 4 Year
1975 ADOZX.Volume_YoY5 5 Year over 5 Year
1976 ADOZX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1977 ADOZX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1978 ADOZX.Volume_SmoothDer Derivative of Smoothed
1979 ADOZX.Volume_Log Log of
1980 ADOZX.Volume_mva200 200 Day MA
1981 ADOZX.Volume_mva050 50 Day MA
1985 ADOZX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1988 ADOZX.Adjusted_Log Log of
1989 ADOZX.Adjusted_mva200 200 Day MA
1990 ADOZX.Adjusted_mva050 50 Day MA
2027 MERFX.Volume_YoY Year over Year
2028 MERFX.Volume_YoY4 4 Year over 4 Year
2029 MERFX.Volume_YoY5 5 Year over 5 Year
2030 MERFX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2031 MERFX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2032 MERFX.Volume_SmoothDer Derivative of Smoothed
2033 MERFX.Volume_Log Log of
2034 MERFX.Volume_mva200 200 Day MA
2035 MERFX.Volume_mva050 50 Day MA
2048 CMNIX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2052 CMNIX.Open_mva200 200 Day MA
2053 CMNIX.Open_mva050 50 Day MA
2057 CMNIX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2061 CMNIX.High_mva200 200 Day MA
2062 CMNIX.High_mva050 50 Day MA
2066 CMNIX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2070 CMNIX.Low_mva200 200 Day MA
2071 CMNIX.Low_mva050 50 Day MA
2075 CMNIX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2079 CMNIX.Close_mva200 200 Day MA
2080 CMNIX.Close_mva050 50 Day MA
2081 CMNIX.Volume_YoY Year over Year
2082 CMNIX.Volume_YoY4 4 Year over 4 Year
2083 CMNIX.Volume_YoY5 5 Year over 5 Year
2084 CMNIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2085 CMNIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2086 CMNIX.Volume_SmoothDer Derivative of Smoothed
2087 CMNIX.Volume_Log Log of
2088 CMNIX.Volume_mva200 200 Day MA
2089 CMNIX.Volume_mva050 50 Day MA
2093 CMNIX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2095 CMNIX.Adjusted_SmoothDer Derivative of Smoothed
2097 CMNIX.Adjusted_mva200 200 Day MA
2098 CMNIX.Adjusted_mva050 50 Day MA
2102 CIHEX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2104 CIHEX.Open_SmoothDer Derivative of Smoothed
2106 CIHEX.Open_mva200 200 Day MA
2107 CIHEX.Open_mva050 50 Day MA
2111 CIHEX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2113 CIHEX.High_SmoothDer Derivative of Smoothed
2115 CIHEX.High_mva200 200 Day MA
2116 CIHEX.High_mva050 50 Day MA
2120 CIHEX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2122 CIHEX.Low_SmoothDer Derivative of Smoothed
2124 CIHEX.Low_mva200 200 Day MA
2125 CIHEX.Low_mva050 50 Day MA
2129 CIHEX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2131 CIHEX.Close_SmoothDer Derivative of Smoothed
2133 CIHEX.Close_mva200 200 Day MA
2134 CIHEX.Close_mva050 50 Day MA
2135 CIHEX.Volume_YoY Year over Year
2136 CIHEX.Volume_YoY4 4 Year over 4 Year
2137 CIHEX.Volume_YoY5 5 Year over 5 Year
2138 CIHEX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2139 CIHEX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2140 CIHEX.Volume_SmoothDer Derivative of Smoothed
2141 CIHEX.Volume_Log Log of
2142 CIHEX.Volume_mva200 200 Day MA
2143 CIHEX.Volume_mva050 50 Day MA
2147 CIHEX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2151 CIHEX.Adjusted_mva200 200 Day MA
2152 CIHEX.Adjusted_mva050 50 Day MA
2156 IMPCH_Smooth Savitsky-Golay Smoothed (p=3, n=365) U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
2158 IMPCH_SmoothDer Derivative of Smoothed U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
2159 IMPCH_Log Log of U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
2161 IMPCH_mva050 U.S. Imports of Goods by Customs Basis from China (Monthly, NSA) 50 Day MA
2167 EXPCH_SmoothDer Derivative of Smoothed U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA)
2176 IMPMX_SmoothDer Derivative of Smoothed U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA)
2178 IMPMX_mva200 U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA) 200 Day MA
2185 EXPMX_SmoothDer Derivative of Smoothed U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA)
2187 EXPMX_mva200 U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA) 200 Day MA
2194 HSN1FNSA_SmoothDer Derivative of Smoothed New One Family Houses Sold: United States (Monthly, NSA)
2201 HNFSUSNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) New One Family Houses for Sale in the United States (Monthly, NSA)
2204 HNFSUSNSA_Log Log of New One Family Houses for Sale in the United States (Monthly, NSA)
2205 HNFSUSNSA_mva200 New One Family Houses for Sale in the United States (Monthly, NSA) 200 Day MA
2206 HNFSUSNSA_mva050 New One Family Houses for Sale in the United States (Monthly, NSA) 50 Day MA
2234 REALLNNSA_YoY Real Estate Loans, All Commercial Banks (Monthly, NSA) Year over Year
2237 REALLNNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Monthly, NSA)
2239 REALLNNSA_SmoothDer Derivative of Smoothed Real Estate Loans, All Commercial Banks (Monthly, NSA)
2240 REALLNNSA_Log Log of Real Estate Loans, All Commercial Banks (Monthly, NSA)
2242 REALLNNSA_mva050 Real Estate Loans, All Commercial Banks (Monthly, NSA) 50 Day MA
2243 REALLN_YoY Real Estate Loans, All Commercial Banks (Monthly, SA) Year over Year
2246 REALLN_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Monthly, SA)
2248 REALLN_SmoothDer Derivative of Smoothed Real Estate Loans, All Commercial Banks (Monthly, SA)
2249 REALLN_Log Log of Real Estate Loans, All Commercial Banks (Monthly, SA)
2251 REALLN_mva050 Real Estate Loans, All Commercial Banks (Monthly, SA) 50 Day MA
2255 RELACBW027NBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Weekly, NSA)
2257 RELACBW027NBOG_SmoothDer Derivative of Smoothed Real Estate Loans, All Commercial Banks (Weekly, NSA)
2260 RELACBW027NBOG_mva050 Real Estate Loans, All Commercial Banks (Weekly, NSA) 50 Day MA
2261 RELACBW027SBOG_YoY Real Estate Loans, All Commercial Banks (Weekly, SA) Year over Year
2264 RELACBW027SBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Weekly, SA)
2266 RELACBW027SBOG_SmoothDer Derivative of Smoothed Real Estate Loans, All Commercial Banks (Weekly, SA)
2267 RELACBW027SBOG_Log Log of Real Estate Loans, All Commercial Banks (Weekly, SA)
2269 RELACBW027SBOG_mva050 Real Estate Loans, All Commercial Banks (Weekly, SA) 50 Day MA
2270 RREACBM027NBOG_YoY Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA) Year over Year
2273 RREACBM027NBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2275 RREACBM027NBOG_SmoothDer Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2276 RREACBM027NBOG_Log Log of Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2279 RREACBM027SBOG_YoY Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA) Year over Year
2284 RREACBM027SBOG_SmoothDer Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA)
2288 RREACBW027SBOG_YoY Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA) Year over Year
2291 RREACBW027SBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
2293 RREACBW027SBOG_SmoothDer Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
2294 RREACBW027SBOG_Log Log of Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
2300 RREACBW027NBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA)
2302 RREACBW027NBOG_SmoothDer Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA)
2305 RREACBW027NBOG_mva050 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA) 50 Day MA
2306 MORTGAGE30US_YoY 30-Year Fixed Rate Mortgage Average in the United States Year over Year
2313 MORTGAGE30US_mva200 30-Year Fixed Rate Mortgage Average in the United States 200 Day MA
2318 CONSUMERNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans, All Commercial Banks
2320 CONSUMERNSA_SmoothDer Derivative of Smoothed Consumer Loans, All Commercial Banks
2321 CONSUMERNSA_Log Log of Consumer Loans, All Commercial Banks
2322 CONSUMERNSA_mva200 Consumer Loans, All Commercial Banks 200 Day MA
2323 CONSUMERNSA_mva050 Consumer Loans, All Commercial Banks 50 Day MA
2327 TOTLLNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Loans and Leases in Bank Credit, All Commercial Banks
2332 TOTLLNSA_mva050 Loans and Leases in Bank Credit, All Commercial Banks 50 Day MA
2336 DPSACBW027SBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Deposits, All Commercial Banks
2339 DPSACBW027SBOG_Log Log of Deposits, All Commercial Banks
2340 DPSACBW027SBOG_mva200 Deposits, All Commercial Banks 200 Day MA
2341 DPSACBW027SBOG_mva050 Deposits, All Commercial Banks 50 Day MA
2347 DRCLACBS_SmoothDer Derivative of Smoothed Delinquency Rate on Consumer Loans, All Commercial Banks, SA
2361 SRPSABSNNCB_YoY4 Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 4 Year over 4 Year
2365 SRPSABSNNCB_SmoothDer Derivative of Smoothed Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
2366 SRPSABSNNCB_Log Log of Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
2368 SRPSABSNNCB_mva050 Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 50 Day MA
2372 ASTLL_Smooth Savitsky-Golay Smoothed (p=3, n=365) All sectors; total loans; liability, Level (NSA)
2375 ASTLL_Log Log of All sectors; total loans; liability, Level (NSA)
2376 ASTLL_mva200 All sectors; total loans; liability, Level (NSA) 200 Day MA
2377 ASTLL_mva050 All sectors; total loans; liability, Level (NSA) 50 Day MA
2378 FBDILNECA_YoY Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA) Year over Year
2384 FBDILNECA_Log Log of Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
2385 FBDILNECA_mva200 Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA) 200 Day MA
2386 FBDILNECA_mva050 Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA) 50 Day MA
2389 ASOLAL_YoY5 All sectors; other loans and advances; liability, Level (NSA) 5 Year over 5 Year
2390 ASOLAL_Smooth Savitsky-Golay Smoothed (p=3, n=365) All sectors; other loans and advances; liability, Level (NSA)
2393 ASOLAL_Log Log of All sectors; other loans and advances; liability, Level (NSA)
2394 ASOLAL_mva200 All sectors; other loans and advances; liability, Level (NSA) 200 Day MA
2395 ASOLAL_mva050 All sectors; other loans and advances; liability, Level (NSA) 50 Day MA
2399 ASTMA_Smooth Savitsky-Golay Smoothed (p=3, n=365) All sectors; total mortgages; asset, Level (NSA)
2402 ASTMA_Log Log of All sectors; total mortgages; asset, Level (NSA)
2403 ASTMA_mva200 All sectors; total mortgages; asset, Level (NSA) 200 Day MA
2404 ASTMA_mva050 All sectors; total mortgages; asset, Level (NSA) 50 Day MA
2408 ASHMA_Smooth Savitsky-Golay Smoothed (p=3, n=365) All sectors; home mortgages; asset, Level (NSA)
2411 ASHMA_Log Log of All sectors; home mortgages; asset, Level (NSA)
2412 ASHMA_mva200 All sectors; home mortgages; asset, Level (NSA) 200 Day MA
2413 ASHMA_mva050 All sectors; home mortgages; asset, Level (NSA) 50 Day MA
2417 ASMRMA_Smooth Savitsky-Golay Smoothed (p=3, n=365) All sectors; multifamily residential mortgages; asset, Level (NSA)
2420 ASMRMA_Log Log of All sectors; multifamily residential mortgages; asset, Level (NSA)
2421 ASMRMA_mva200 All sectors; multifamily residential mortgages; asset, Level (NSA) 200 Day MA
2422 ASMRMA_mva050 All sectors; multifamily residential mortgages; asset, Level (NSA) 50 Day MA
2426 ASCMA_Smooth Savitsky-Golay Smoothed (p=3, n=365) All sectors; commercial mortgages; asset, Level (NSA)
2429 ASCMA_Log Log of All sectors; commercial mortgages; asset, Level (NSA)
2430 ASCMA_mva200 All sectors; commercial mortgages; asset, Level (NSA) 200 Day MA
2431 ASCMA_mva050 All sectors; commercial mortgages; asset, Level (NSA) 50 Day MA
2435 ASFMA_Smooth Savitsky-Golay Smoothed (p=3, n=365) All sectors; farm mortgages; asset, Level (NSA)
2438 ASFMA_Log Log of All sectors; farm mortgages; asset, Level (NSA)
2439 ASFMA_mva200 All sectors; farm mortgages; asset, Level (NSA) 200 Day MA
2440 ASFMA_mva050 All sectors; farm mortgages; asset, Level (NSA) 50 Day MA
2446 CCLBSHNO_SmoothDer Derivative of Smoothed Households and nonprofit organizations; consumer credit; liability, Level (NSA)
2447 CCLBSHNO_Log Log of Households and nonprofit organizations; consumer credit; liability, Level (NSA)
2449 CCLBSHNO_mva050 Households and nonprofit organizations; consumer credit; liability, Level (NSA) 50 Day MA
2453 FBDSILQ027S_Smooth Savitsky-Golay Smoothed (p=3, n=365) Domestic financial sectors debt securities; liability, Level (NSA)
2456 FBDSILQ027S_Log Log of Domestic financial sectors debt securities; liability, Level (NSA)
2457 FBDSILQ027S_mva200 Domestic financial sectors debt securities; liability, Level (NSA) 200 Day MA
2458 FBDSILQ027S_mva050 Domestic financial sectors debt securities; liability, Level (NSA) 50 Day MA
2460 FBLL_YoY4 Domestic financial sectors loans; liability, Level (NSA) 4 Year over 4 Year
2461 FBLL_YoY5 Domestic financial sectors loans; liability, Level (NSA) 5 Year over 5 Year
2462 FBLL_Smooth Savitsky-Golay Smoothed (p=3, n=365) Domestic financial sectors loans; liability, Level (NSA)
2465 FBLL_Log Log of Domestic financial sectors loans; liability, Level (NSA)
2467 FBLL_mva050 Domestic financial sectors loans; liability, Level (NSA) 50 Day MA
2474 NCBDBIQ027S_Log Log of Nonfinancial corporate business; debt securities; liability, Level
2475 NCBDBIQ027S_mva200 Nonfinancial corporate business; debt securities; liability, Level 200 Day MA
2476 NCBDBIQ027S_mva050 Nonfinancial corporate business; debt securities; liability, Level 50 Day MA
2522 TNX.Volume_YoY Year over Year
2523 TNX.Volume_YoY4 4 Year over 4 Year
2524 TNX.Volume_YoY5 5 Year over 5 Year
2525 TNX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2526 TNX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2527 TNX.Volume_SmoothDer Derivative of Smoothed
2528 TNX.Volume_Log Log of
2529 TNX.Volume_mva200 200 Day MA
2530 TNX.Volume_mva050 50 Day MA
2546 CLF.Open_Log Log of
2547 CLF.Open_mva200 200 Day MA
2556 CLF.High_mva200 200 Day MA
2564 CLF.Low_Log Log of
2565 CLF.Low_mva200 200 Day MA
2573 CLF.Close_Log Log of
2574 CLF.Close_mva200 200 Day MA
2582 CLF.Volume_Log Log of
2591 CLF.Adjusted_Log Log of
2592 CLF.Adjusted_mva200 200 Day MA
2606 DGS1_Smooth Savitsky-Golay Smoothed (p=3, n=365) 1-Year Treasury Constant Maturity Rate
2608 DGS1_SmoothDer Derivative of Smoothed 1-Year Treasury Constant Maturity Rate
2615 DGS2_Smooth Savitsky-Golay Smoothed (p=3, n=365) 2-Year Treasury Constant Maturity Rate
2617 DGS2_SmoothDer Derivative of Smoothed 2-Year Treasury Constant Maturity Rate
2619 DGS2_mva200 2-Year Treasury Constant Maturity Rate 200 Day MA
2624 TB3MS_Smooth Savitsky-Golay Smoothed (p=3, n=365) 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2626 TB3MS_SmoothDer Derivative of Smoothed 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2627 TB3MS_Log Log of 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2629 TB3MS_mva050 3-Month Treasury Bill: Secondary Market Rate (Monthly) 50 Day MA
2633 DTB3_Smooth Savitsky-Golay Smoothed (p=3, n=365) 3-Month Treasury Bill: Secondary Market Rate (Daily)
2635 DTB3_SmoothDer Derivative of Smoothed 3-Month Treasury Bill: Secondary Market Rate (Daily)
2636 DTB3_Log Log of 3-Month Treasury Bill: Secondary Market Rate (Daily)
2645 IRX.Open_Log Log of
2654 IRX.High_Log Log of
2660 IRX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2662 IRX.Low_SmoothDer Derivative of Smoothed
2663 IRX.Low_Log Log of
2669 IRX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2671 IRX.Close_SmoothDer Derivative of Smoothed
2672 IRX.Close_Log Log of
2675 IRX.Volume_YoY Year over Year
2676 IRX.Volume_YoY4 4 Year over 4 Year
2677 IRX.Volume_YoY5 5 Year over 5 Year
2678 IRX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2679 IRX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2680 IRX.Volume_SmoothDer Derivative of Smoothed
2681 IRX.Volume_Log Log of
2682 IRX.Volume_mva200 200 Day MA
2683 IRX.Volume_mva050 50 Day MA
2687 IRX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2689 IRX.Adjusted_SmoothDer Derivative of Smoothed
2690 IRX.Adjusted_Log Log of
2699 DCOILWTICO_Log Log of Crude Oil Prices: West Texas Intermediate (WTI) Cushing, Oklahoma
2700 DCOILWTICO_mva200 Crude Oil Prices: West Texas Intermediate (WTI) Cushing, Oklahoma 200 Day MA
2709 DCOILBRENTEU_mva200 Crude Oil Prices: Brent - Europe 200 Day MA
2717 NEWORDER_Log Log of Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft
2718 NEWORDER_mva200 Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft 200 Day MA
2719 NEWORDER_mva050 Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft 50 Day MA
2741 GSPC.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2743 GSPC.Open_SmoothDer Derivative of Smoothed
2745 GSPC.Open_mva200 200 Day MA
2746 GSPC.Open_mva050 50 Day MA
2750 GSPC.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2752 GSPC.High_SmoothDer Derivative of Smoothed
2754 GSPC.High_mva200 200 Day MA
2755 GSPC.High_mva050 50 Day MA
2759 GSPC.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2761 GSPC.Low_SmoothDer Derivative of Smoothed
2763 GSPC.Low_mva200 200 Day MA
2764 GSPC.Low_mva050 50 Day MA
2768 GSPC.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2770 GSPC.Close_SmoothDer Derivative of Smoothed
2772 GSPC.Close_mva200 200 Day MA
2773 GSPC.Close_mva050 50 Day MA
2786 GSPC.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2788 GSPC.Adjusted_SmoothDer Derivative of Smoothed
2790 GSPC.Adjusted_mva200 200 Day MA
2791 GSPC.Adjusted_mva050 50 Day MA
2794 RLG.Open_YoY5 5 Year over 5 Year
2795 RLG.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2797 RLG.Open_SmoothDer Derivative of Smoothed
2799 RLG.Open_mva200 200 Day MA
2800 RLG.Open_mva050 50 Day MA
2803 RLG.High_YoY5 5 Year over 5 Year
2804 RLG.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2806 RLG.High_SmoothDer Derivative of Smoothed
2808 RLG.High_mva200 200 Day MA
2809 RLG.High_mva050 50 Day MA
2812 RLG.Low_YoY5 5 Year over 5 Year
2813 RLG.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2815 RLG.Low_SmoothDer Derivative of Smoothed
2817 RLG.Low_mva200 200 Day MA
2818 RLG.Low_mva050 50 Day MA
2822 RLG.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2824 RLG.Close_SmoothDer Derivative of Smoothed
2826 RLG.Close_mva200 200 Day MA
2827 RLG.Close_mva050 50 Day MA
2828 RLG.Volume_YoY Year over Year
2829 RLG.Volume_YoY4 4 Year over 4 Year
2830 RLG.Volume_YoY5 5 Year over 5 Year
2831 RLG.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2832 RLG.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2833 RLG.Volume_SmoothDer Derivative of Smoothed
2834 RLG.Volume_Log Log of
2835 RLG.Volume_mva200 200 Day MA
2836 RLG.Volume_mva050 50 Day MA
2840 RLG.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2842 RLG.Adjusted_SmoothDer Derivative of Smoothed
2844 RLG.Adjusted_mva200 200 Day MA
2845 RLG.Adjusted_mva050 50 Day MA
2853 DJI.Open_mva200 200 Day MA
2854 DJI.Open_mva050 50 Day MA
2862 DJI.High_mva200 200 Day MA
2863 DJI.High_mva050 50 Day MA
2871 DJI.Low_mva200 200 Day MA
2872 DJI.Low_mva050 50 Day MA
2880 DJI.Close_mva200 200 Day MA
2881 DJI.Close_mva050 50 Day MA
2898 DJI.Adjusted_mva200 200 Day MA
2899 DJI.Adjusted_mva050 50 Day MA
2907 STOXX50E.Open_mva200 200 Day MA
2908 STOXX50E.Open_mva050 50 Day MA
2916 STOXX50E.High_mva200 200 Day MA
2917 STOXX50E.High_mva050 50 Day MA
2925 STOXX50E.Low_mva200 200 Day MA
2926 STOXX50E.Low_mva050 50 Day MA
2934 STOXX50E.Close_mva200 200 Day MA
2935 STOXX50E.Close_mva050 50 Day MA
2942 STOXX50E.Volume_Log Log of
2952 STOXX50E.Adjusted_mva200 200 Day MA
2953 STOXX50E.Adjusted_mva050 50 Day MA
2961 EFA.Open_mva200 200 Day MA
2962 EFA.Open_mva050 50 Day MA
2970 EFA.High_mva200 200 Day MA
2971 EFA.High_mva050 50 Day MA
2979 EFA.Low_mva200 200 Day MA
2980 EFA.Low_mva050 50 Day MA
2988 EFA.Close_mva200 200 Day MA
2989 EFA.Close_mva050 50 Day MA
3002 EFA.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3006 EFA.Adjusted_mva200 200 Day MA
3007 EFA.Adjusted_mva050 50 Day MA
3014 GDP_Log Log of Gross Domestic Product
3015 GDP_mva200 Gross Domestic Product 200 Day MA
3016 GDP_mva050 Gross Domestic Product 50 Day MA
3017 FNDEFX_YoY Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate) Year over Year
3031 FDEFX_SmoothDer Derivative of Smoothed Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
3032 FDEFX_Log Log of Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
3033 FDEFX_mva200 Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 200 Day MA
3034 FDEFX_mva050 Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 50 Day MA
3040 GDPNOW_SmoothDer Derivative of Smoothed Fed Atlanta GDPNow
3041 GDPNOW_Log Log of Fed Atlanta GDPNow
3050 GDPC1_Log Log of Real Gross Domestic Product
3051 GDPC1_mva200 Real Gross Domestic Product 200 Day MA
3052 GDPC1_mva050 Real Gross Domestic Product 50 Day MA
3059 GDPDEF_Log Log of Gross Domestic Product: Implicit Price Deflator
3060 GDPDEF_mva200 Gross Domestic Product: Implicit Price Deflator 200 Day MA
3061 GDPDEF_mva050 Gross Domestic Product: Implicit Price Deflator 50 Day MA
3065 VIG.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3067 VIG.Open_SmoothDer Derivative of Smoothed
3069 VIG.Open_mva200 200 Day MA
3070 VIG.Open_mva050 50 Day MA
3074 VIG.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3076 VIG.High_SmoothDer Derivative of Smoothed
3078 VIG.High_mva200 200 Day MA
3079 VIG.High_mva050 50 Day MA
3083 VIG.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3085 VIG.Low_SmoothDer Derivative of Smoothed
3087 VIG.Low_mva200 200 Day MA
3088 VIG.Low_mva050 50 Day MA
3092 VIG.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3094 VIG.Close_SmoothDer Derivative of Smoothed
3096 VIG.Close_mva200 200 Day MA
3097 VIG.Close_mva050 50 Day MA
3110 VIG.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3112 VIG.Adjusted_SmoothDer Derivative of Smoothed
3114 VIG.Adjusted_mva200 200 Day MA
3115 VIG.Adjusted_mva050 50 Day MA
3116 WLRRAL_YoY Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Year over Year
3118 WLRRAL_YoY5 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) 5 Year over 5 Year
3119 WLRRAL_Smooth Savitsky-Golay Smoothed (p=3, n=365) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3120 WLRRAL_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3121 WLRRAL_SmoothDer Derivative of Smoothed Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3122 WLRRAL_Log Log of Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA)
3123 WLRRAL_mva200 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) 200 Day MA
3124 WLRRAL_mva050 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) 50 Day MA
3128 FEDFUNDS_Smooth Savitsky-Golay Smoothed (p=3, n=365) Effective Federal Funds Rate
3130 FEDFUNDS_SmoothDer Derivative of Smoothed Effective Federal Funds Rate
3137 GPDI_Smooth Savitsky-Golay Smoothed (p=3, n=365) Gross Private Domestic Investment
3139 GPDI_SmoothDer Derivative of Smoothed Gross Private Domestic Investment
3148 W790RC1Q027SBEA_SmoothDer Derivative of Smoothed Net domestic investment: Private: Domestic busines
3149 W790RC1Q027SBEA_Log Log of Net domestic investment: Private: Domestic busines
3157 MZMV_SmoothDer Derivative of Smoothed Velocity of MZM Money Stock
3158 MZMV_Log Log of Velocity of MZM Money Stock
3160 MZMV_mva050 Velocity of MZM Money Stock 50 Day MA
3166 M1_SmoothDer Derivative of Smoothed M1 Money Stock
3167 M1_Log Log of M1 Money Stock
3176 M2_Log Log of M2 Money Stock
3185 OPHNFB_Log Log of Nonfarm Business Sector: Real Output Per Hour of All Persons
3186 OPHNFB_mva200 Nonfarm Business Sector: Real Output Per Hour of All Persons 200 Day MA
3187 OPHNFB_mva050 Nonfarm Business Sector: Real Output Per Hour of All Persons 50 Day MA
3191 IPMAN_Smooth Savitsky-Golay Smoothed (p=3, n=365) Industrial Production: Manufacturing (NAICS)
3193 IPMAN_SmoothDer Derivative of Smoothed Industrial Production: Manufacturing (NAICS)
3194 IPMAN_Log Log of Industrial Production: Manufacturing (NAICS)
3195 IPMAN_mva200 Industrial Production: Manufacturing (NAICS) 200 Day MA
3196 IPMAN_mva050 Industrial Production: Manufacturing (NAICS) 50 Day MA
3204 IWD.Open_mva200 200 Day MA
3205 IWD.Open_mva050 50 Day MA
3213 IWD.High_mva200 200 Day MA
3214 IWD.High_mva050 50 Day MA
3222 IWD.Low_mva200 200 Day MA
3223 IWD.Low_mva050 50 Day MA
3231 IWD.Close_mva200 200 Day MA
3232 IWD.Close_mva050 50 Day MA
3249 IWD.Adjusted_mva200 200 Day MA
3250 IWD.Adjusted_mva050 50 Day MA
3258 GS5_mva200 5-Year Treasury Constant Maturity Rate 200 Day MA
3274 VIXCLS_SmoothDer Derivative of Smoothed CBOE Volatility Index
3283 VXX.Open_SmoothDer Derivative of Smoothed
3292 VXX.High_SmoothDer Derivative of Smoothed
3301 VXX.Low_SmoothDer Derivative of Smoothed
3310 VXX.Close_SmoothDer Derivative of Smoothed
3315 VXX.Volume_YoY4 4 Year over 4 Year
3316 VXX.Volume_YoY5 5 Year over 5 Year
3320 VXX.Volume_Log Log of
3321 VXX.Volume_mva200 200 Day MA
3328 VXX.Adjusted_SmoothDer Derivative of Smoothed
3337 HOUST1F_SmoothDer Derivative of Smoothed Privately Owned Housing Starts: 1-Unit Structures
3347 GFDEBTN_Log Log of Federal Debt: Total Public Debt
3348 GFDEBTN_mva200 Federal Debt: Total Public Debt 200 Day MA
3349 GFDEBTN_mva050 Federal Debt: Total Public Debt 50 Day MA
3355 HOUST_SmoothDer Derivative of Smoothed Housing Starts: Total: New Privately Owned Housing Units Started
3365 MSPUS_Log Log of Median Sales Price of Houses Sold for the United States
3366 MSPUS_mva200 Median Sales Price of Houses Sold for the United States 200 Day MA
3367 MSPUS_mva050 Median Sales Price of Houses Sold for the United States 50 Day MA
3382 DGORDER_SmoothDer Derivative of Smoothed Manufacturers’ New Orders: Durable Goods (SA)
3384 DGORDER_mva200 Manufacturers’ New Orders: Durable Goods (SA) 200 Day MA
3392 CSUSHPINSA_Log Log of S&P/Case-Shiller U.S. National Home Price Index (NSA)
3393 CSUSHPINSA_mva200 S&P/Case-Shiller U.S. National Home Price Index (NSA) 200 Day MA
3394 CSUSHPINSA_mva050 S&P/Case-Shiller U.S. National Home Price Index (NSA) 50 Day MA
3400 GFDEGDQ188S_SmoothDer Derivative of Smoothed Federal Debt: Total Public Debt as Percent of Gross Domestic Product
3408 FYFSD_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) Federal Surplus or Deficit
3410 FYFSD_Log Log of Federal Surplus or Deficit
3412 FYFSD_mva050 Federal Surplus or Deficit 50 Day MA
3413 FYFSGDA188S_YoY Federal Surplus or Deficit [-] as Percent of Gross Domestic Product Year over Year
3417 FYFSGDA188S_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
3419 FYFSGDA188S_Log Log of Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
3420 FYFSGDA188S_mva200 Federal Surplus or Deficit [-] as Percent of Gross Domestic Product 200 Day MA
3421 FYFSGDA188S_mva050 Federal Surplus or Deficit [-] as Percent of Gross Domestic Product 50 Day MA
3425 GOLDAMGBD228NLBM_Smooth Savitsky-Golay Smoothed (p=3, n=365) Gold Fixing Price 10:30 A.M. (London time) in London Bullion Market
3427 GOLDAMGBD228NLBM_SmoothDer Derivative of Smoothed Gold Fixing Price 10:30 A.M. (London time) in London Bullion Market
3436 GDX.Open_SmoothDer Derivative of Smoothed
3445 GDX.High_SmoothDer Derivative of Smoothed
3454 GDX.Low_SmoothDer Derivative of Smoothed
3463 GDX.Close_SmoothDer Derivative of Smoothed
3470 GDX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3472 GDX.Volume_SmoothDer Derivative of Smoothed
3481 GDX.Adjusted_SmoothDer Derivative of Smoothed
3492 XLE.Open_mva200 200 Day MA
3501 XLE.High_mva200 200 Day MA
3510 XLE.Low_mva200 200 Day MA
3519 XLE.Close_mva200 200 Day MA
3537 XLE.Adjusted_mva200 200 Day MA
3546 GSG.Open_mva200 200 Day MA
3555 GSG.High_mva200 200 Day MA
3564 GSG.Low_mva200 200 Day MA
3573 GSG.Close_mva200 200 Day MA
3591 GSG.Adjusted_mva200 200 Day MA
3596 WALCL_Smooth Savitsky-Golay Smoothed (p=3, n=365) All Federal Reserve Banks: Total Assets
3597 WALCL_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) All Federal Reserve Banks: Total Assets
3598 WALCL_SmoothDer Derivative of Smoothed All Federal Reserve Banks: Total Assets
3599 WALCL_Log Log of All Federal Reserve Banks: Total Assets
3600 WALCL_mva200 All Federal Reserve Banks: Total Assets 200 Day MA
3601 WALCL_mva050 All Federal Reserve Banks: Total Assets 50 Day MA
3604 OUTMS_YoY5 Manufacturing Sector: Real Output 5 Year over 5 Year
3607 OUTMS_SmoothDer Derivative of Smoothed Manufacturing Sector: Real Output
3608 OUTMS_Log Log of Manufacturing Sector: Real Output
3609 OUTMS_mva200 Manufacturing Sector: Real Output 200 Day MA
3610 OUTMS_mva050 Manufacturing Sector: Real Output 50 Day MA
3613 MANEMP_YoY5 All Employees: Manufacturing 5 Year over 5 Year
3614 MANEMP_Smooth Savitsky-Golay Smoothed (p=3, n=365) All Employees: Manufacturing
3616 MANEMP_SmoothDer Derivative of Smoothed All Employees: Manufacturing
3617 MANEMP_Log Log of All Employees: Manufacturing
3618 MANEMP_mva200 All Employees: Manufacturing 200 Day MA
3619 MANEMP_mva050 All Employees: Manufacturing 50 Day MA
3622 PRS30006163_YoY5 Manufacturing Sector: Real Output Per Person 5 Year over 5 Year
3625 PRS30006163_SmoothDer Derivative of Smoothed Manufacturing Sector: Real Output Per Person
3626 PRS30006163_Log Log of Manufacturing Sector: Real Output Per Person
3627 PRS30006163_mva200 Manufacturing Sector: Real Output Per Person 200 Day MA
3628 PRS30006163_mva050 Manufacturing Sector: Real Output Per Person 50 Day MA
3648 SOFR_YoY4 Secured Overnight Financing Rate 4 Year over 4 Year
3649 SOFR_YoY5 Secured Overnight Financing Rate 5 Year over 5 Year
3650 SOFR_Smooth Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Rate
3652 SOFR_SmoothDer Derivative of Smoothed Secured Overnight Financing Rate
3653 SOFR_Log Log of Secured Overnight Financing Rate
3655 SOFR_mva050 Secured Overnight Financing Rate 50 Day MA
3659 SOFRVOL_Smooth Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Volume
3666 SOFR99_YoY4 Secured Overnight Financing Rate: 99th Percentile 4 Year over 4 Year
3667 SOFR99_YoY5 Secured Overnight Financing Rate: 99th Percentile 5 Year over 5 Year
3670 SOFR99_SmoothDer Derivative of Smoothed Secured Overnight Financing Rate: 99th Percentile
3671 SOFR99_Log Log of Secured Overnight Financing Rate: 99th Percentile
3673 SOFR99_mva050 Secured Overnight Financing Rate: 99th Percentile 50 Day MA
3677 SOFR75_Smooth Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Rate: 75th Percentile
3679 SOFR75_SmoothDer Derivative of Smoothed Secured Overnight Financing Rate: 75th Percentile
3684 SOFR25_YoY4 Secured Overnight Financing Rate: 25th Percentile 4 Year over 4 Year
3685 SOFR25_YoY5 Secured Overnight Financing Rate: 25th Percentile 5 Year over 5 Year
3686 SOFR25_Smooth Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Rate: 25th Percentile
3688 SOFR25_SmoothDer Derivative of Smoothed Secured Overnight Financing Rate: 25th Percentile
3689 SOFR25_Log Log of Secured Overnight Financing Rate: 25th Percentile
3691 SOFR25_mva050 Secured Overnight Financing Rate: 25th Percentile 50 Day MA
3695 SOFR1_Smooth Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Rate: 1st Percentile
3697 SOFR1_SmoothDer Derivative of Smoothed Secured Overnight Financing Rate: 1st Percentile
3698 SOFR1_Log Log of Secured Overnight Financing Rate: 1st Percentile
3704 OBFR_Smooth Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate
3706 OBFR_SmoothDer Derivative of Smoothed Overnight Bank Funding Rate
3713 OBFR99_Smooth Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate: 99th Percentile
3715 OBFR99_SmoothDer Derivative of Smoothed Overnight Bank Funding Rate: 99th Percentile
3722 OBFR75_Smooth Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate: 75th Percentile
3724 OBFR75_SmoothDer Derivative of Smoothed Overnight Bank Funding Rate: 75th Percentile
3731 OBFR25_Smooth Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate: 25th Percentile
3733 OBFR25_SmoothDer Derivative of Smoothed Overnight Bank Funding Rate: 25th Percentile
3740 OBFR1_Smooth Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate: 1st Percentile
3742 OBFR1_SmoothDer Derivative of Smoothed Overnight Bank Funding Rate: 1st Percentile
3746 RPONTSYD_YoY Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations Year over Year
3752 RPONTSYD_Log Log of Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations
3755 IOER_YoY Interest Rate on Excess Reserves Year over Year
3757 IOER_YoY5 Interest Rate on Excess Reserves 5 Year over 5 Year
3758 IOER_Smooth Savitsky-Golay Smoothed (p=3, n=365) Interest Rate on Excess Reserves
3760 IOER_SmoothDer Derivative of Smoothed Interest Rate on Excess Reserves
3761 IOER_Log Log of Interest Rate on Excess Reserves
3762 IOER_mva200 Interest Rate on Excess Reserves 200 Day MA
3763 IOER_mva050 Interest Rate on Excess Reserves 50 Day MA
3766 WRESBAL_YoY5 Reserve Balances with Federal Reserve Banks 5 Year over 5 Year
3768 WRESBAL_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) Reserve Balances with Federal Reserve Banks
3770 WRESBAL_Log Log of Reserve Balances with Federal Reserve Banks
3771 WRESBAL_mva200 Reserve Balances with Federal Reserve Banks 200 Day MA
3772 WRESBAL_mva050 Reserve Balances with Federal Reserve Banks 50 Day MA
3779 EXCSRESNW_Log Log of Excess Reserves of Depository Institutions
3780 EXCSRESNW_mva200 Excess Reserves of Depository Institutions 200 Day MA
3781 EXCSRESNW_mva050 Excess Reserves of Depository Institutions 50 Day MA
3782 ECBASSETS_YoY Central Bank Assets for Euro Area (11-19 Countries) Year over Year
3788 ECBASSETS_Log Log of Central Bank Assets for Euro Area (11-19 Countries)
3789 ECBASSETS_mva200 Central Bank Assets for Euro Area (11-19 Countries) 200 Day MA
3790 ECBASSETS_mva050 Central Bank Assets for Euro Area (11-19 Countries) 50 Day MA
3796 EUNNGDP_SmoothDer Derivative of Smoothed Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
3797 EUNNGDP_Log Log of Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
3798 EUNNGDP_mva200 Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries) 200 Day MA
3799 EUNNGDP_mva050 Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries) 50 Day MA
3803 CEU0600000007_Smooth Savitsky-Golay Smoothed (p=3, n=365) Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
3805 CEU0600000007_SmoothDer Derivative of Smoothed Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
3806 CEU0600000007_Log Log of Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
3807 CEU0600000007_mva200 Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing 200 Day MA
3808 CEU0600000007_mva050 Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing 50 Day MA
3824 CURRENCY_Log Log of Currency Component of M1 (Seasonally Adjusted)
3825 CURRENCY_mva200 Currency Component of M1 (Seasonally Adjusted) 200 Day MA
3826 CURRENCY_mva050 Currency Component of M1 (Seasonally Adjusted) 50 Day MA
3833 WCURRNS_Log Log of Currency Component of M1
3834 WCURRNS_mva200 Currency Component of M1 200 Day MA
3835 WCURRNS_mva050 Currency Component of M1 50 Day MA
3838 BOGMBASE_YoY5 Monetary Base; Total 5 Year over 5 Year
3842 BOGMBASE_Log Log of Monetary Base; Total
3843 BOGMBASE_mva200 Monetary Base; Total 200 Day MA
3844 BOGMBASE_mva050 Monetary Base; Total 50 Day MA
3847 PRS88003193_YoY5 Nonfinancial Corporations Sector: Unit Profits 5 Year over 5 Year
3851 PRS88003193_Log Log of Nonfinancial Corporations Sector: Unit Profits
3852 PRS88003193_mva200 Nonfinancial Corporations Sector: Unit Profits 200 Day MA
3853 PRS88003193_mva050 Nonfinancial Corporations Sector: Unit Profits 50 Day MA
3860 PPIACO_Log Log of Producer Price Index for All Commodities
3861 PPIACO_mva200 Producer Price Index for All Commodities 200 Day MA
3862 PPIACO_mva050 Producer Price Index for All Commodities 50 Day MA
3869 PCUOMFGOMFG_Log Log of Producer Price Index by Industry: Total Manufacturing Industries
3870 PCUOMFGOMFG_mva200 Producer Price Index by Industry: Total Manufacturing Industries 200 Day MA
3871 PCUOMFGOMFG_mva050 Producer Price Index by Industry: Total Manufacturing Industries 50 Day MA
3878 POPTHM_Smooth Savitsky-Golay Smoothed (p=3, n=365) Population (U.S.)
3879 POPTHM_Smooth Savitsky-Golay Smoothed (p=3, n=365) Population (U.S.)
3884 POPTHM_Log Log of Population (U.S.)
3885 POPTHM_Log Log of Population (U.S.)
3886 POPTHM_mva200 Population (U.S.) 200 Day MA
3887 POPTHM_mva200 Population (U.S.) 200 Day MA
3888 POPTHM_mva050 Population (U.S.) 50 Day MA
3889 POPTHM_mva050 Population (U.S.) 50 Day MA
3896 POPTHM.1_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3897 POPTHM.1_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3902 POPTHM.1_Log Log of
3903 POPTHM.1_Log Log of
3904 POPTHM.1_mva200 200 Day MA
3905 POPTHM.1_mva200 200 Day MA
3906 POPTHM.1_mva050 50 Day MA
3907 POPTHM.1_mva050 50 Day MA
3911 CLF16OV_Smooth Savitsky-Golay Smoothed (p=3, n=365) Civilian Labor Force Level, SA
3913 CLF16OV_SmoothDer Derivative of Smoothed Civilian Labor Force Level, SA
3914 CLF16OV_Log Log of Civilian Labor Force Level, SA
3915 CLF16OV_mva200 Civilian Labor Force Level, SA 200 Day MA
3916 CLF16OV_mva050 Civilian Labor Force Level, SA 50 Day MA
3922 LNU01000000_SmoothDer Derivative of Smoothed Civilian Labor Force Level, NSA
3924 LNU01000000_mva200 Civilian Labor Force Level, NSA 200 Day MA
3926 LNU03000000_YoY Unemployment Level (NSA) Year over Year
3951 RSAFS_mva200 Advance Retail Sales: Retail and Food Services 200 Day MA
3958 FRGSHPUSM649NCIS_SmoothDer Derivative of Smoothed Cass Freight Index: Shipments
3965 BOPGTB_Smooth Savitsky-Golay Smoothed (p=3, n=365) Trade Balance: Goods, Balance of Payments Basis (SA)
3967 BOPGTB_SmoothDer Derivative of Smoothed Trade Balance: Goods, Balance of Payments Basis (SA)
3968 BOPGTB_Log Log of Trade Balance: Goods, Balance of Payments Basis (SA)
3970 BOPGTB_mva050 Trade Balance: Goods, Balance of Payments Basis (SA) 50 Day MA
3973 TERMCBPER24NS_YoY5 Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 5 Year over 5 Year
3974 TERMCBPER24NS_Smooth Savitsky-Golay Smoothed (p=3, n=365) Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan
3977 TERMCBPER24NS_Log Log of Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan
3978 TERMCBPER24NS_mva200 Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 200 Day MA
3979 TERMCBPER24NS_mva050 Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan 50 Day MA
3980 A065RC1A027NBEA_YoY Personal income (NSA) Year over Year
3986 A065RC1A027NBEA_Log Log of Personal income (NSA)
3987 A065RC1A027NBEA_mva200 Personal income (NSA) 200 Day MA
3988 A065RC1A027NBEA_mva050 Personal income (NSA) 50 Day MA
4004 PCE_Log Log of Personal Consumption Expenditures (SA)
4005 PCE_mva200 Personal Consumption Expenditures (SA) 200 Day MA
4006 PCE_mva050 Personal Consumption Expenditures (SA) 50 Day MA
4008 A053RC1Q027SBEA_YoY4 National income: Corporate profits before tax (without IVA and CCAdj) 4 Year over 4 Year
4013 A053RC1Q027SBEA_Log Log of National income: Corporate profits before tax (without IVA and CCAdj)
4014 A053RC1Q027SBEA_mva200 National income: Corporate profits before tax (without IVA and CCAdj) 200 Day MA
4015 A053RC1Q027SBEA_mva050 National income: Corporate profits before tax (without IVA and CCAdj) 50 Day MA
4022 CPROFIT_Log Log of Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj)
4023 CPROFIT_mva200 Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj) 200 Day MA
4024 CPROFIT_mva050 Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj) 50 Day MA
4028 SPY.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4030 SPY.Open_SmoothDer Derivative of Smoothed
4032 SPY.Open_mva200 200 Day MA
4033 SPY.Open_mva050 50 Day MA
4037 SPY.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4039 SPY.High_SmoothDer Derivative of Smoothed
4041 SPY.High_mva200 200 Day MA
4042 SPY.High_mva050 50 Day MA
4046 SPY.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4048 SPY.Low_SmoothDer Derivative of Smoothed
4050 SPY.Low_mva200 200 Day MA
4051 SPY.Low_mva050 50 Day MA
4055 SPY.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4057 SPY.Close_SmoothDer Derivative of Smoothed
4059 SPY.Close_mva200 200 Day MA
4060 SPY.Close_mva050 50 Day MA
4073 SPY.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4075 SPY.Adjusted_SmoothDer Derivative of Smoothed
4077 SPY.Adjusted_mva200 200 Day MA
4078 SPY.Adjusted_mva050 50 Day MA
4086 MDY.Open_mva200 200 Day MA
4095 MDY.High_mva200 200 Day MA
4104 MDY.Low_mva200 200 Day MA
4113 MDY.Close_mva200 200 Day MA
4131 MDY.Adjusted_mva200 200 Day MA
4140 EES.Open_mva200 200 Day MA
4149 EES.High_mva200 200 Day MA
4158 EES.Low_mva200 200 Day MA
4167 EES.Close_mva200 200 Day MA
4175 EES.Volume_Log Log of
4185 EES.Adjusted_mva200 200 Day MA
4194 IJR.Open_mva200 200 Day MA
4203 IJR.High_mva200 200 Day MA
4212 IJR.Low_mva200 200 Day MA
4221 IJR.Close_mva200 200 Day MA
4239 IJR.Adjusted_mva200 200 Day MA
4244 VGSTX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4246 VGSTX.Open_SmoothDer Derivative of Smoothed
4248 VGSTX.Open_mva200 200 Day MA
4249 VGSTX.Open_mva050 50 Day MA
4253 VGSTX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4255 VGSTX.High_SmoothDer Derivative of Smoothed
4257 VGSTX.High_mva200 200 Day MA
4258 VGSTX.High_mva050 50 Day MA
4262 VGSTX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4264 VGSTX.Low_SmoothDer Derivative of Smoothed
4266 VGSTX.Low_mva200 200 Day MA
4267 VGSTX.Low_mva050 50 Day MA
4271 VGSTX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4273 VGSTX.Close_SmoothDer Derivative of Smoothed
4275 VGSTX.Close_mva200 200 Day MA
4276 VGSTX.Close_mva050 50 Day MA
4277 VGSTX.Volume_YoY Year over Year
4278 VGSTX.Volume_YoY4 4 Year over 4 Year
4279 VGSTX.Volume_YoY5 5 Year over 5 Year
4280 VGSTX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4281 VGSTX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
4282 VGSTX.Volume_SmoothDer Derivative of Smoothed
4283 VGSTX.Volume_Log Log of
4284 VGSTX.Volume_mva200 200 Day MA
4285 VGSTX.Volume_mva050 50 Day MA
4289 VGSTX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4291 VGSTX.Adjusted_SmoothDer Derivative of Smoothed
4293 VGSTX.Adjusted_mva200 200 Day MA
4294 VGSTX.Adjusted_mva050 50 Day MA
4298 VFINX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4300 VFINX.Open_SmoothDer Derivative of Smoothed
4302 VFINX.Open_mva200 200 Day MA
4303 VFINX.Open_mva050 50 Day MA
4307 VFINX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4309 VFINX.High_SmoothDer Derivative of Smoothed
4311 VFINX.High_mva200 200 Day MA
4312 VFINX.High_mva050 50 Day MA
4316 VFINX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4318 VFINX.Low_SmoothDer Derivative of Smoothed
4320 VFINX.Low_mva200 200 Day MA
4321 VFINX.Low_mva050 50 Day MA
4325 VFINX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4327 VFINX.Close_SmoothDer Derivative of Smoothed
4329 VFINX.Close_mva200 200 Day MA
4330 VFINX.Close_mva050 50 Day MA
4331 VFINX.Volume_YoY Year over Year
4332 VFINX.Volume_YoY4 4 Year over 4 Year
4333 VFINX.Volume_YoY5 5 Year over 5 Year
4334 VFINX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4335 VFINX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
4336 VFINX.Volume_SmoothDer Derivative of Smoothed
4337 VFINX.Volume_Log Log of
4338 VFINX.Volume_mva200 200 Day MA
4339 VFINX.Volume_mva050 50 Day MA
4343 VFINX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4345 VFINX.Adjusted_SmoothDer Derivative of Smoothed
4347 VFINX.Adjusted_mva200 200 Day MA
4348 VFINX.Adjusted_mva050 50 Day MA
4356 VOE.Open_mva200 200 Day MA
4365 VOE.High_mva200 200 Day MA
4374 VOE.Low_mva200 200 Day MA
4383 VOE.Close_mva200 200 Day MA
4401 VOE.Adjusted_mva200 200 Day MA
4402 VOE.Adjusted_mva050 50 Day MA
4406 VOT.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4408 VOT.Open_SmoothDer Derivative of Smoothed
4410 VOT.Open_mva200 200 Day MA
4411 VOT.Open_mva050 50 Day MA
4415 VOT.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4417 VOT.High_SmoothDer Derivative of Smoothed
4419 VOT.High_mva200 200 Day MA
4420 VOT.High_mva050 50 Day MA
4424 VOT.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4426 VOT.Low_SmoothDer Derivative of Smoothed
4428 VOT.Low_mva200 200 Day MA
4429 VOT.Low_mva050 50 Day MA
4433 VOT.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4435 VOT.Close_SmoothDer Derivative of Smoothed
4437 VOT.Close_mva200 200 Day MA
4438 VOT.Close_mva050 50 Day MA
4451 VOT.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4453 VOT.Adjusted_SmoothDer Derivative of Smoothed
4455 VOT.Adjusted_mva200 200 Day MA
4456 VOT.Adjusted_mva050 50 Day MA
4460 TMFGX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4462 TMFGX.Open_SmoothDer Derivative of Smoothed
4464 TMFGX.Open_mva200 200 Day MA
4465 TMFGX.Open_mva050 50 Day MA
4469 TMFGX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4471 TMFGX.High_SmoothDer Derivative of Smoothed
4473 TMFGX.High_mva200 200 Day MA
4474 TMFGX.High_mva050 50 Day MA
4478 TMFGX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4480 TMFGX.Low_SmoothDer Derivative of Smoothed
4482 TMFGX.Low_mva200 200 Day MA
4483 TMFGX.Low_mva050 50 Day MA
4487 TMFGX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4489 TMFGX.Close_SmoothDer Derivative of Smoothed
4491 TMFGX.Close_mva200 200 Day MA
4492 TMFGX.Close_mva050 50 Day MA
4493 TMFGX.Volume_YoY Year over Year
4494 TMFGX.Volume_YoY4 4 Year over 4 Year
4495 TMFGX.Volume_YoY5 5 Year over 5 Year
4496 TMFGX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4497 TMFGX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
4498 TMFGX.Volume_SmoothDer Derivative of Smoothed
4499 TMFGX.Volume_Log Log of
4500 TMFGX.Volume_mva200 200 Day MA
4501 TMFGX.Volume_mva050 50 Day MA
4505 TMFGX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4507 TMFGX.Adjusted_SmoothDer Derivative of Smoothed
4509 TMFGX.Adjusted_mva200 200 Day MA
4510 TMFGX.Adjusted_mva050 50 Day MA
4518 IWM.Open_mva200 200 Day MA
4536 IWM.Low_mva200 200 Day MA
4563 IWM.Adjusted_mva200 200 Day MA
4568 ONEQ.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4570 ONEQ.Open_SmoothDer Derivative of Smoothed
4572 ONEQ.Open_mva200 200 Day MA
4573 ONEQ.Open_mva050 50 Day MA
4577 ONEQ.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4579 ONEQ.High_SmoothDer Derivative of Smoothed
4581 ONEQ.High_mva200 200 Day MA
4582 ONEQ.High_mva050 50 Day MA
4586 ONEQ.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4588 ONEQ.Low_SmoothDer Derivative of Smoothed
4590 ONEQ.Low_mva200 200 Day MA
4591 ONEQ.Low_mva050 50 Day MA
4595 ONEQ.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4597 ONEQ.Close_SmoothDer Derivative of Smoothed
4599 ONEQ.Close_mva200 200 Day MA
4600 ONEQ.Close_mva050 50 Day MA
4613 ONEQ.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4615 ONEQ.Adjusted_SmoothDer Derivative of Smoothed
4617 ONEQ.Adjusted_mva200 200 Day MA
4618 ONEQ.Adjusted_mva050 50 Day MA
4626 HAINX.Open_mva200 200 Day MA
4627 HAINX.Open_mva050 50 Day MA
4635 HAINX.High_mva200 200 Day MA
4636 HAINX.High_mva050 50 Day MA
4644 HAINX.Low_mva200 200 Day MA
4645 HAINX.Low_mva050 50 Day MA
4653 HAINX.Close_mva200 200 Day MA
4654 HAINX.Close_mva050 50 Day MA
4655 HAINX.Volume_YoY Year over Year
4656 HAINX.Volume_YoY4 4 Year over 4 Year
4657 HAINX.Volume_YoY5 5 Year over 5 Year
4658 HAINX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4659 HAINX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
4660 HAINX.Volume_SmoothDer Derivative of Smoothed
4661 HAINX.Volume_Log Log of
4662 HAINX.Volume_mva200 200 Day MA
4663 HAINX.Volume_mva050 50 Day MA
4671 HAINX.Adjusted_mva200 200 Day MA
4672 HAINX.Adjusted_mva050 50 Day MA
4680 VEU.Open_mva200 200 Day MA
4689 VEU.High_mva200 200 Day MA
4698 VEU.Low_mva200 200 Day MA
4707 VEU.Close_mva200 200 Day MA
4725 VEU.Adjusted_mva200 200 Day MA
4788 IVOO.Open_mva200 200 Day MA
4797 IVOO.High_mva200 200 Day MA
4806 IVOO.Low_mva200 200 Day MA
4815 IVOO.Close_mva200 200 Day MA
4823 IVOO.Volume_Log Log of
4833 IVOO.Adjusted_mva200 200 Day MA
4838 VO.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4842 VO.Open_mva200 200 Day MA
4843 VO.Open_mva050 50 Day MA
4847 VO.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4851 VO.High_mva200 200 Day MA
4852 VO.High_mva050 50 Day MA
4856 VO.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4860 VO.Low_mva200 200 Day MA
4861 VO.Low_mva050 50 Day MA
4865 VO.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4869 VO.Close_mva200 200 Day MA
4870 VO.Close_mva050 50 Day MA
4877 VO.Volume_Log Log of
4883 VO.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4887 VO.Adjusted_mva200 200 Day MA
4888 VO.Adjusted_mva050 50 Day MA
4892 CZA.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4896 CZA.Open_mva200 200 Day MA
4897 CZA.Open_mva050 50 Day MA
4901 CZA.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4905 CZA.High_mva200 200 Day MA
4906 CZA.High_mva050 50 Day MA
4910 CZA.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4914 CZA.Low_mva200 200 Day MA
4915 CZA.Low_mva050 50 Day MA
4919 CZA.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4923 CZA.Close_mva200 200 Day MA
4924 CZA.Close_mva050 50 Day MA
4931 CZA.Volume_Log Log of
4937 CZA.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4941 CZA.Adjusted_mva200 200 Day MA
4942 CZA.Adjusted_mva050 50 Day MA
4950 VYM.Open_mva200 200 Day MA
4959 VYM.High_mva200 200 Day MA
4968 VYM.Low_mva200 200 Day MA
4969 VYM.Low_mva050 50 Day MA
4977 VYM.Close_mva200 200 Day MA
4978 VYM.Close_mva050 50 Day MA
4995 VYM.Adjusted_mva200 200 Day MA
4996 VYM.Adjusted_mva050 50 Day MA
5000 ACWI.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5004 ACWI.Open_mva200 200 Day MA
5005 ACWI.Open_mva050 50 Day MA
5009 ACWI.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5013 ACWI.High_mva200 200 Day MA
5014 ACWI.High_mva050 50 Day MA
5018 ACWI.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5022 ACWI.Low_mva200 200 Day MA
5023 ACWI.Low_mva050 50 Day MA
5027 ACWI.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5031 ACWI.Close_mva200 200 Day MA
5032 ACWI.Close_mva050 50 Day MA
5045 ACWI.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5049 ACWI.Adjusted_mva200 200 Day MA
5050 ACWI.Adjusted_mva050 50 Day MA
5058 SLY.Open_mva200 200 Day MA
5067 SLY.High_mva200 200 Day MA
5076 SLY.Low_mva200 200 Day MA
5085 SLY.Close_mva200 200 Day MA
5093 SLY.Volume_Log Log of
5103 SLY.Adjusted_mva200 200 Day MA
5108 QQQ.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5110 QQQ.Open_SmoothDer Derivative of Smoothed
5112 QQQ.Open_mva200 200 Day MA
5113 QQQ.Open_mva050 50 Day MA
5117 QQQ.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5119 QQQ.High_SmoothDer Derivative of Smoothed
5121 QQQ.High_mva200 200 Day MA
5122 QQQ.High_mva050 50 Day MA
5126 QQQ.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5128 QQQ.Low_SmoothDer Derivative of Smoothed
5130 QQQ.Low_mva200 200 Day MA
5131 QQQ.Low_mva050 50 Day MA
5135 QQQ.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5137 QQQ.Close_SmoothDer Derivative of Smoothed
5139 QQQ.Close_mva200 200 Day MA
5140 QQQ.Close_mva050 50 Day MA
5153 QQQ.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5155 QQQ.Adjusted_SmoothDer Derivative of Smoothed
5157 QQQ.Adjusted_mva200 200 Day MA
5158 QQQ.Adjusted_mva050 50 Day MA
5162 HYMB.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5164 HYMB.Open_SmoothDer Derivative of Smoothed
5166 HYMB.Open_mva200 200 Day MA
5171 HYMB.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5173 HYMB.High_SmoothDer Derivative of Smoothed
5175 HYMB.High_mva200 200 Day MA
5180 HYMB.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5182 HYMB.Low_SmoothDer Derivative of Smoothed
5184 HYMB.Low_mva200 200 Day MA
5189 HYMB.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5191 HYMB.Close_SmoothDer Derivative of Smoothed
5193 HYMB.Close_mva200 200 Day MA
5201 HYMB.Volume_Log Log of
5207 HYMB.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5209 HYMB.Adjusted_SmoothDer Derivative of Smoothed
5211 HYMB.Adjusted_mva200 200 Day MA
5218 GOLD.Open_SmoothDer Derivative of Smoothed
5219 GOLD.Open_Log Log of
5227 GOLD.High_SmoothDer Derivative of Smoothed
5236 GOLD.Low_SmoothDer Derivative of Smoothed
5245 GOLD.Close_SmoothDer Derivative of Smoothed
5255 GOLD.Volume_Log Log of
5263 GOLD.Adjusted_SmoothDer Derivative of Smoothed
5273 BKR.Open_Log Log of
5309 BKR.Volume_Log Log of
5328 SLB.Open_mva200 200 Day MA
5346 SLB.Low_mva200 200 Day MA
5381 HAL.Open_Log Log of
5417 HAL.Volume_Log Log of
5435 IP.Open_Log Log of
5436 IP.Open_mva200 200 Day MA
5445 IP.High_mva200 200 Day MA
5454 IP.Low_mva200 200 Day MA
5463 IP.Close_mva200 200 Day MA
5481 IP.Adjusted_mva200 200 Day MA
5486 PKG.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5490 PKG.Open_mva200 200 Day MA
5495 PKG.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5499 PKG.High_mva200 200 Day MA
5504 PKG.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5508 PKG.Low_mva200 200 Day MA
5513 PKG.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5517 PKG.Close_mva200 200 Day MA
5524 PKG.Volume_SmoothDer Derivative of Smoothed
5531 PKG.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5535 PKG.Adjusted_mva200 200 Day MA
5542 UPS.Open_SmoothDer Derivative of Smoothed
5544 UPS.Open_mva200 200 Day MA
5551 UPS.High_SmoothDer Derivative of Smoothed
5553 UPS.High_mva200 200 Day MA
5560 UPS.Low_SmoothDer Derivative of Smoothed
5562 UPS.Low_mva200 200 Day MA
5569 UPS.Close_SmoothDer Derivative of Smoothed
5571 UPS.Close_mva200 200 Day MA
5587 UPS.Adjusted_SmoothDer Derivative of Smoothed
5589 UPS.Adjusted_mva200 200 Day MA
5598 FDX.Open_mva200 200 Day MA
5607 FDX.High_mva200 200 Day MA
5616 FDX.Low_mva200 200 Day MA
5625 FDX.Close_mva200 200 Day MA
5632 FDX.Volume_SmoothDer Derivative of Smoothed
5643 FDX.Adjusted_mva200 200 Day MA
5651 T.Open_Log Log of
5704 VZ.Open_SmoothDer Derivative of Smoothed
5713 VZ.High_SmoothDer Derivative of Smoothed
5722 VZ.Low_SmoothDer Derivative of Smoothed
5731 VZ.Close_SmoothDer Derivative of Smoothed
5749 VZ.Adjusted_SmoothDer Derivative of Smoothed
5765 MULTPLSP500PERATIOMONTH_Smooth Savitsky-Golay Smoothed (p=3, n=365) S&P 500 TTM P/E
5767 MULTPLSP500PERATIOMONTH_SmoothDer Derivative of Smoothed S&P 500 TTM P/E
5771 MULTPLSP500SALESQUARTER_YoY S&P 500 TTM Sales (Not Inflation Adjusted) Year over Year
5777 MULTPLSP500SALESQUARTER_Log Log of S&P 500 TTM Sales (Not Inflation Adjusted)
5778 MULTPLSP500SALESQUARTER_mva200 S&P 500 TTM Sales (Not Inflation Adjusted) 200 Day MA
5779 MULTPLSP500SALESQUARTER_mva050 S&P 500 TTM Sales (Not Inflation Adjusted) 50 Day MA
5794 MULTPLSP500DIVMONTH_SmoothDer Derivative of Smoothed S&P 500 Dividend by Month (Inflation Adjusted)
5805 CHRISCMEHG1_mva200 Copper Futures, Continuous Contract #1 (HG1) (Front Month) 200 Day MA
5807 WWDIWLDISAIRGOODMTK1_YoY Air transport, freight Year over Year
5813 WWDIWLDISAIRGOODMTK1_Log Log of Air transport, freight
5814 WWDIWLDISAIRGOODMTK1_mva200 Air transport, freight 200 Day MA
5815 WWDIWLDISAIRGOODMTK1_mva050 Air transport, freight 50 Day MA
5834 PETA143B00001M_YoY U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly Year over Year
5835 PETA143B00001M_YoY4 U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 4 Year over 4 Year
5836 PETA143B00001M_YoY5 U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 5 Year over 5 Year
5840 PETA143B00001M_Log Log of U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly
5841 PETA143B00001M_mva200 U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 200 Day MA
5842 PETA143B00001M_mva050 U.S. Midgrade Gasoline Retail Sales by Refiners, Monthly 50 Day MA
5848 PETA133B00001M_SmoothDer Derivative of Smoothed U.S. Premium Gasoline Bulk Sales (Volume) by Refiners, Monthly
5853 TOTALOGNRPUSM_YoY4 Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly 4 Year over 4 Year
5858 TOTALOGNRPUSM_Log Log of Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly
5859 TOTALOGNRPUSM_mva200 Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly 200 Day MA
5860 TOTALOGNRPUSM_mva050 Crude Oil and Natural Gas Rotary Rigs in Operation, Total, Monthly 50 Day MA
5862 TOTALPANRPUSM_YoY4 Crude Oil Rotary Rigs in Operation, Monthly 4 Year over 4 Year
5867 TOTALPANRPUSM_Log Log of Crude Oil Rotary Rigs in Operation, Monthly
5868 TOTALPANRPUSM_mva200 Crude Oil Rotary Rigs in Operation, Monthly 200 Day MA
5869 TOTALPANRPUSM_mva050 Crude Oil Rotary Rigs in Operation, Monthly 50 Day MA
5876 TOTALNGNRPUSM_Log Log of Natural Gas Rotary Rigs in Operation, Monthly
5877 TOTALNGNRPUSM_mva200 Natural Gas Rotary Rigs in Operation, Monthly 200 Day MA
5878 TOTALNGNRPUSM_mva050 Natural Gas Rotary Rigs in Operation, Monthly 50 Day MA
5885 BKRTotal_Log Log of Total Rig Count
5886 BKRTotal_mva200 Total Rig Count 200 Day MA
5887 BKRTotal_mva050 Total Rig Count 50 Day MA
5894 BKRGas_Log Log of Gas Rig Count
5895 BKRGas_mva200 Gas Rig Count 200 Day MA
5896 BKRGas_mva050 Gas Rig Count 50 Day MA
5903 BKROil_Log Log of Oil Rig Count
5905 BKROil_mva050 Oil Rig Count 50 Day MA
5906 FARMINCOME_YoY Net Farm Income Year over Year
5912 FARMINCOME_Log Log of Net Farm Income
5913 FARMINCOME_mva200 Net Farm Income 200 Day MA
5914 FARMINCOME_mva050 Net Farm Income 50 Day MA
5921 OPEARNINGSPERSHARE_Log Log of Operating Earnings per Share
5922 OPEARNINGSPERSHARE_mva200 Operating Earnings per Share 200 Day MA
5923 OPEARNINGSPERSHARE_mva050 Operating Earnings per Share 50 Day MA
5930 AREARNINGSPERSHARE_Log Log of As-Reported Earnings per Share
5931 AREARNINGSPERSHARE_mva200 As-Reported Earnings per Share 200 Day MA
5932 AREARNINGSPERSHARE_mva050 As-Reported Earnings per Share 50 Day MA
5933 CASHDIVIDENDSPERSHR_YoY Cash Dividends per Share Year over Year
5939 CASHDIVIDENDSPERSHR_Log Log of Cash Dividends per Share
5940 CASHDIVIDENDSPERSHR_mva200 Cash Dividends per Share 200 Day MA
5941 CASHDIVIDENDSPERSHR_mva050 Cash Dividends per Share 50 Day MA
5957 BOOKVALPERSHR_Log Log of Book value per Share
5958 BOOKVALPERSHR_mva200 Book value per Share 200 Day MA
5959 BOOKVALPERSHR_mva050 Book value per Share 50 Day MA
5975 PRICE_Log Log of Price
5976 PRICE_mva200 Price 200 Day MA
5977 PRICE_mva050 Price 50 Day MA
5978 OPEARNINGSTTM_YoY TTM Operating Earnings Year over Year
5984 OPEARNINGSTTM_Log Log of TTM Operating Earnings
5985 OPEARNINGSTTM_mva200 TTM Operating Earnings 200 Day MA
5986 OPEARNINGSTTM_mva050 TTM Operating Earnings 50 Day MA
5987 AREARNINGSTTM_YoY TTM Reported Earnings Year over Year
5993 AREARNINGSTTM_Log Log of TTM Reported Earnings
5994 AREARNINGSTTM_mva200 TTM Reported Earnings 200 Day MA
5995 AREARNINGSTTM_mva050 TTM Reported Earnings 50 Day MA
6003 FINRAMarginDebt_mva200 Margin Debt 200 Day MA
6014 OCCEquityVolume_YoY Equity Options Volume Year over Year
6020 OCCEquityVolume_Log Log of Equity Options Volume
6021 OCCEquityVolume_mva200 Equity Options Volume 200 Day MA
6022 OCCEquityVolume_mva050 Equity Options Volume 50 Day MA
6023 OCCNonEquityVolume_YoY Non-Equity Options Volume Year over Year
6029 OCCNonEquityVolume_Log Log of Non-Equity Options Volume
6030 OCCNonEquityVolume_mva200 Non-Equity Options Volume 200 Day MA
6031 OCCNonEquityVolume_mva050 Non-Equity Options Volume 50 Day MA
6044 BUSLOANS.minus.BUSLOANSNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans (Montlhy) SA - NSA
6046 BUSLOANS.minus.BUSLOANSNSA_SmoothDer Derivative of Smoothed Business Loans (Montlhy) SA - NSA
6047 BUSLOANS.minus.BUSLOANSNSA_Log Log of Business Loans (Montlhy) SA - NSA
6049 BUSLOANS.minus.BUSLOANSNSA_mva050 Business Loans (Montlhy) SA - NSA 50 Day MA
6053 BUSLOANS.minus.BUSLOANSNSA.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans (Montlhy) SA - NSA divided by GDP
6055 BUSLOANS.minus.BUSLOANSNSA.by.GDP_SmoothDer Derivative of Smoothed Business Loans (Montlhy) SA - NSA divided by GDP
6056 BUSLOANS.minus.BUSLOANSNSA.by.GDP_Log Log of Business Loans (Montlhy) SA - NSA divided by GDP
6058 BUSLOANS.minus.BUSLOANSNSA.by.GDP_mva050 Business Loans (Montlhy) SA - NSA divided by GDP 50 Day MA
6059 BUSLOANS.by.GDP_YoY Business Loans Normalized by GDP Year over Year
6086 BUSLOANSNSA.by.GDP_YoY Business Loans Normalized by GDP Year over Year
6132 W875RX1.by.GDP_YoY4 Real Personal Income Normalized by GDP 4 Year over 4 Year
6133 W875RX1.by.GDP_YoY5 Real Personal Income Normalized by GDP 5 Year over 5 Year
6134 W875RX1.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Personal Income Normalized by GDP
6136 W875RX1.by.GDP_SmoothDer Derivative of Smoothed Real Personal Income Normalized by GDP
6140 A065RC1A027NBEA.by.GDP_YoY Personal Income (NSA) Normalized by GDP Year over Year
6145 A065RC1A027NBEA.by.GDP_SmoothDer Derivative of Smoothed Personal Income (NSA) Normalized by GDP
6159 A053RC1Q027SBEA.by.GDP_YoY4 National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP 4 Year over 4 Year
6164 A053RC1Q027SBEA.by.GDP_Log Log of National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP
6165 A053RC1Q027SBEA.by.GDP_mva200 National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP 200 Day MA
6166 A053RC1Q027SBEA.by.GDP_mva050 National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP 50 Day MA
6173 CPROFIT.by.GDP_Log Log of National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP
6174 CPROFIT.by.GDP_mva200 National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP 200 Day MA
6175 CPROFIT.by.GDP_mva050 National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP 50 Day MA
6176 CONSUMERNSA.by.GDP_YoY Consumer Loans Not Seasonally Adjusted divided by GDP Year over Year
6179 CONSUMERNSA.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans Not Seasonally Adjusted divided by GDP
6181 CONSUMERNSA.by.GDP_SmoothDer Derivative of Smoothed Consumer Loans Not Seasonally Adjusted divided by GDP
6182 CONSUMERNSA.by.GDP_Log Log of Consumer Loans Not Seasonally Adjusted divided by GDP
6184 CONSUMERNSA.by.GDP_mva050 Consumer Loans Not Seasonally Adjusted divided by GDP 50 Day MA
6185 RREACBM027NBOG.by.GDP_YoY Residental Real Estate Loans (Monthly, NSA) divided by GDP Year over Year
6188 RREACBM027NBOG.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Monthly, NSA) divided by GDP
6190 RREACBM027NBOG.by.GDP_SmoothDer Derivative of Smoothed Residental Real Estate Loans (Monthly, NSA) divided by GDP
6194 RREACBM027SBOG.by.GDP_YoY Residental Real Estate Loans (Monthly, SA) divided by GDP Year over Year
6199 RREACBM027SBOG.by.GDP_SmoothDer Derivative of Smoothed Residental Real Estate Loans (Monthly, SA) divided by GDP
6203 RREACBW027SBOG.by.GDP_YoY Residental Real Estate Loans (Weekly, SA) divided by GDP Year over Year
6204 RREACBW027SBOG.by.GDP_YoY4 Residental Real Estate Loans (Weekly, SA) divided by GDP 4 Year over 4 Year
6206 RREACBW027SBOG.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Weekly, SA) divided by GDP
6208 RREACBW027SBOG.by.GDP_SmoothDer Derivative of Smoothed Residental Real Estate Loans (Weekly, SA) divided by GDP
6212 RREACBW027NBOG.by.GDP_YoY Residental Real Estate Loans (Weekly, NSA) divided by GDP Year over Year
6213 RREACBW027NBOG.by.GDP_YoY4 Residental Real Estate Loans (Weekly, NSA) divided by GDP 4 Year over 4 Year
6215 RREACBW027NBOG.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Weekly, NSA) divided by GDP
6217 RREACBW027NBOG.by.GDP_SmoothDer Derivative of Smoothed Residental Real Estate Loans (Weekly, NSA) divided by GDP
6233 DGORDER.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Durable Goods (Monthly, NSA) divided by GDP
6235 DGORDER.by.GDP_SmoothDer Derivative of Smoothed Durable Goods (Monthly, NSA) divided by GDP
6237 DGORDER.by.GDP_mva200 Durable Goods (Monthly, NSA) divided by GDP 200 Day MA
6239 ASHMA.by.GDP_YoY Home Mortgages (Quarterly, NSA) divided by GDP Year over Year
6240 ASHMA.by.GDP_YoY4 Home Mortgages (Quarterly, NSA) divided by GDP 4 Year over 4 Year
6244 ASHMA.by.GDP_SmoothDer Derivative of Smoothed Home Mortgages (Quarterly, NSA) divided by GDP
6248 ASHMA.INTEREST_YoY Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens Year over Year
6255 ASHMA.INTEREST_mva200 Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 200 Day MA
6257 ASHMA.INTEREST.by.GDP_YoY Year over Year
6269 CONSUMERNSA.INTEREST_Smooth Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans (Not Seasonally Adjusted) Interest Burdens
6271 CONSUMERNSA.INTEREST_SmoothDer Derivative of Smoothed Consumer Loans (Not Seasonally Adjusted) Interest Burdens
6272 CONSUMERNSA.INTEREST_Log Log of Consumer Loans (Not Seasonally Adjusted) Interest Burdens
6273 CONSUMERNSA.INTEREST_mva200 Consumer Loans (Not Seasonally Adjusted) Interest Burdens 200 Day MA
6274 CONSUMERNSA.INTEREST_mva050 Consumer Loans (Not Seasonally Adjusted) Interest Burdens 50 Day MA
6277 CONSUMERNSA.INTEREST.by.GDP_YoY5 Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 5 Year over 5 Year
6278 CONSUMERNSA.INTEREST.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
6280 CONSUMERNSA.INTEREST.by.GDP_SmoothDer Derivative of Smoothed Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
6281 CONSUMERNSA.INTEREST.by.GDP_Log Log of Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
6283 CONSUMERNSA.INTEREST.by.GDP_mva050 Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 50 Day MA
6289 TOTLNNSA_SmoothDer Derivative of Smoothed Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA)
6293 TOTLNNSA.by.GDP_YoY Total Loans Not Seasonally Adjusted divided by GDP Year over Year
6298 TOTLNNSA.by.GDP_SmoothDer Derivative of Smoothed Total Loans Not Seasonally Adjusted divided by GDP
6322 WRESBAL.by.GDP_YoY5 Reserve Balances with Federal Reserve Banks Divided by GDP 5 Year over 5 Year
6323 WRESBAL.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Reserve Balances with Federal Reserve Banks Divided by GDP
6324 WRESBAL.by.GDP_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) Reserve Balances with Federal Reserve Banks Divided by GDP
6326 WRESBAL.by.GDP_Log Log of Reserve Balances with Federal Reserve Banks Divided by GDP
6327 WRESBAL.by.GDP_mva200 Reserve Balances with Federal Reserve Banks Divided by GDP 200 Day MA
6328 WRESBAL.by.GDP_mva050 Reserve Balances with Federal Reserve Banks Divided by GDP 50 Day MA
6338 WLRRAL.by.GDP_YoY Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP Year over Year
6340 WLRRAL.by.GDP_YoY5 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP 5 Year over 5 Year
6341 WLRRAL.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
6342 WLRRAL.by.GDP_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
6343 WLRRAL.by.GDP_SmoothDer Derivative of Smoothed Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
6344 WLRRAL.by.GDP_Log Log of Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP
6345 WLRRAL.by.GDP_mva200 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP 200 Day MA
6346 WLRRAL.by.GDP_mva050 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP 50 Day MA
6354 SOFR99.minus.SOFR1_mva200 Secured Overnight Financing Rate: 99th Percentile - 1st Percentile 200 Day MA
6362 EXPCH.minus.IMPCH_Log Log of U.S. Exports to China (FAS Basis) - U.S. Imports to China (Customs Basis)
6368 EXPMX.minus.IMPMX_Smooth Savitsky-Golay Smoothed (p=3, n=365)
6371 EXPMX.minus.IMPMX_Log Log of
6373 EXPMX.minus.IMPMX_mva050 50 Day MA
6375 SRPSABSNNCB.by.GDP_YoY4 Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP 4 Year over 4 Year
6379 SRPSABSNNCB.by.GDP_SmoothDer Derivative of Smoothed Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP
6380 SRPSABSNNCB.by.GDP_Log Log of Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP
6383 ASTLL.by.GDP_YoY All sectors; total loans; liability, Level (NSA) Divided by GDP Year over Year
6388 ASTLL.by.GDP_SmoothDer Derivative of Smoothed All sectors; total loans; liability, Level (NSA) Divided by GDP
6392 ASFMA.by.GDP_YoY All sectors; farm mortgages; asset, Level (NSA) Divided by GDP Year over Year
6397 ASFMA.by.GDP_SmoothDer Derivative of Smoothed All sectors; farm mortgages; asset, Level (NSA) Divided by GDP
6402 ASFMA.by.ASTLL_YoY4 All sectors; total loans Divided by farm mortgages 4 Year over 4 Year
6406 ASFMA.by.ASTLL_SmoothDer Derivative of Smoothed All sectors; total loans Divided by farm mortgages
6407 ASFMA.by.ASTLL_Log Log of All sectors; total loans Divided by farm mortgages
6409 ASFMA.by.ASTLL_mva050 All sectors; total loans Divided by farm mortgages 50 Day MA
6410 ASFMA.INTEREST_YoY Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens Year over Year
6417 ASFMA.INTEREST_mva200 Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 200 Day MA
6419 ASFMA.INTEREST.by.GDP_YoY Farm Mortgages (Quarterly, NSA) Interest Burden Divided by GDP Year over Year
6428 FARMINCOME.by.GDP_YoY Farm Income (Annual, NSA) Divided by GDP Year over Year
6433 FARMINCOME.by.GDP_SmoothDer Derivative of Smoothed Farm Income (Annual, NSA) Divided by GDP
6439 BOGMBASE.by.GDP_YoY5 BOGMBASE Divided by GDP 5 Year over 5 Year
6443 BOGMBASE.by.GDP_Log Log of BOGMBASE Divided by GDP
6444 BOGMBASE.by.GDP_mva200 BOGMBASE Divided by GDP 200 Day MA
6445 BOGMBASE.by.GDP_mva050 BOGMBASE Divided by GDP 50 Day MA
6449 WALCL.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) All Federal Reserve Banks: Total Assets Divided by GDP
6450 WALCL.by.GDP_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) All Federal Reserve Banks: Total Assets Divided by GDP
6451 WALCL.by.GDP_SmoothDer Derivative of Smoothed All Federal Reserve Banks: Total Assets Divided by GDP
6452 WALCL.by.GDP_Log Log of All Federal Reserve Banks: Total Assets Divided by GDP
6453 WALCL.by.GDP_mva200 All Federal Reserve Banks: Total Assets Divided by GDP 200 Day MA
6454 WALCL.by.GDP_mva050 All Federal Reserve Banks: Total Assets Divided by GDP 50 Day MA
6455 ECBASSETS.by.EUNNGDP_YoY Central Bank Assets for Euro Area (11-19 Countries) Divided by GDP Year over Year
6470 DGS30TO10_Log Log of Yield Curve, 30 and 10 Year Treasury (DGS30-DGS10)
6479 DGS10TO1_Log Log of Yield Curve, 10 and 1 Year Treasury (DGS10-DGS1)
6488 DGS10TO2_Log Log of Yield Curve, 10 and 2 Year Treasury (DGS10-DGS2)
6497 DGS10TOTB3MS_Log Log of Yield Curve, 10 and 3 Month Treasury (DGS10-TB3MS)
6506 DGS10TODTB3_Log Log of Yield Curve, 10 and 3 Month Treasury (DGS10-DTB3)
6512 DGS10ByAAA_Smooth Savitsky-Golay Smoothed (p=3, n=365) AAA ratio to 10 year treasury (AAA/DGS10)
6514 DGS10ByAAA_SmoothDer Derivative of Smoothed AAA ratio to 10 year treasury (AAA/DGS10)
6518 LNU03000000BYPOPTHM_YoY Unemployment level (NSA) / Population Year over Year
6539 NPPTTLBYPOPTHM_Smooth Savitsky-Golay Smoothed (p=3, n=365) ADP Private Employment / Population
6541 NPPTTLBYPOPTHM_SmoothDer Derivative of Smoothed ADP Private Employment / Population
6542 NPPTTLBYPOPTHM_Log Log of ADP Private Employment / Population
6543 NPPTTLBYPOPTHM_mva200 ADP Private Employment / Population 200 Day MA
6544 NPPTTLBYPOPTHM_mva050 ADP Private Employment / Population 50 Day MA
6570 CHRISCMEHG1.by.CPIAUCSL_mva200 Copper, $/lb, Normalized by consumer price index 200 Day MA
6579 DCOILBRENTEU.by.PPIACO_mva200 Crude Oil - Brent, $/bbl, Normalized by producer price index c.o. 200 Day MA
6587 DCOILWTICO.by.PPIACO_Log Log of Crude Oil - WTI, $/bbl, Normalized by producer price index c.o.
6588 DCOILWTICO.by.PPIACO_mva200 Crude Oil - WTI, $/bbl, Normalized by producer price index c.o. 200 Day MA
6595 GOLDAMGBD228NLBM.by.PPIACO_SmoothDer Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by commodities producer price index
6604 GOLDAMGBD228NLBM.by.CPIAUCSL_SmoothDer Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by consumer price index
6611 GOLDAMGBD228NLBM.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Gold, USD/Troy OUnce, Normalized by GDP
6613 GOLDAMGBD228NLBM.by.GDP_SmoothDer Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by GDP
6624 GSG.Close.by.GDPDEF_mva200 GSCI Commodity-Indexed Trust, Normalized by GDP def 200 Day MA
6642 GDPBYPOPTHM_mva200 GDP/Population 200 Day MA
6665 GSPC.CloseBYMDY.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365) GSPC by MDY
6667 GSPC.CloseBYMDY.Close_SmoothDer Derivative of Smoothed GSPC by MDY
6670 GSPC.CloseBYMDY.Close_mva050 GSPC by MDY 50 Day MA
6674 QQQ.CloseBYMDY.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365) QQQ by MDY
6675 QQQ.CloseBYMDY.Close_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) QQQ by MDY
6676 QQQ.CloseBYMDY.Close_SmoothDer Derivative of Smoothed QQQ by MDY
6679 QQQ.CloseBYMDY.Close_mva050 QQQ by MDY 50 Day MA
6686 GSPC.DailySwing_Log Log of S&P 500 (^GSPC) Daily Swing: (High - Low) / Open
6692 GSPC.Open.by.GDPDEF_Smooth Savitsky-Golay Smoothed (p=3, n=365) S&P 500 (^GSPC) Open divided by GDP deflator
6694 GSPC.Open.by.GDPDEF_SmoothDer Derivative of Smoothed S&P 500 (^GSPC) Open divided by GDP deflator
6696 GSPC.Open.by.GDPDEF_mva200 S&P 500 (^GSPC) Open divided by GDP deflator 200 Day MA
6697 GSPC.Open.by.GDPDEF_mva050 S&P 500 (^GSPC) Open divided by GDP deflator 50 Day MA
6701 GSPC.Close.by.GDPDEF_Smooth Savitsky-Golay Smoothed (p=3, n=365) S&P 500 (^GSPC) Close divided by GDP deflator
6703 GSPC.Close.by.GDPDEF_SmoothDer Derivative of Smoothed S&P 500 (^GSPC) Close divided by GDP deflator
6705 GSPC.Close.by.GDPDEF_mva200 S&P 500 (^GSPC) Close divided by GDP deflator 200 Day MA
6706 GSPC.Close.by.GDPDEF_mva050 S&P 500 (^GSPC) Close divided by GDP deflator 50 Day MA
6710 HNFSUSNSA.minus.HSN1FNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Houses for sale - houses sold
6713 HNFSUSNSA.minus.HSN1FNSA_Log Log of Houses for sale - houses sold
6714 HNFSUSNSA.minus.HSN1FNSA_mva200 Houses for sale - houses sold 200 Day MA
6715 HNFSUSNSA.minus.HSN1FNSA_mva050 Houses for sale - houses sold 50 Day MA
6728 MSPUS.times.HNFSUSNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) New privately owned 1-family units for sale times median price
6731 MSPUS.times.HNFSUSNSA_Log Log of New privately owned 1-family units for sale times median price
6732 MSPUS.times.HNFSUSNSA_mva200 New privately owned 1-family units for sale times median price 200 Day MA
6733 MSPUS.times.HNFSUSNSA_mva050 New privately owned 1-family units for sale times median price 50 Day MA
6734 TOTLNNSA.PLUS.WRESBAL Total Loans Plus All Reserves (TOTLNNSA + WRESBAL)
6735 TOTLLNSA.PLUS.WRESBAL Total Loans Plus All Reserves (TOTLLNSA + WRESBAL)
6741 GSPC.Open_mva050_mva200_sig Sell Signal S&P 500 50 SMA - 200 SMA
6742 MULTPLSP500PERATIOMONTH_Mean S&P 500 TTM P/E Average (Excludes Values Greater Than 50)

Equities

Equity indexes normalized by GDP

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

The last two years compare favorably with the period around the late 1950’s. Need to dig into this one.

datay <- "GSPC.Close"
ylim <- c(2000, d.GSPC.max)
my.data <- plotSimilarPeriods(df.data, dfRecession, df.symbols, datay, ylim, i.window = 60)
my.data[[1]]

Look at how the different segments of the market move

datay <- "GSPC.CloseBYMDY.Close_YoY"
ylim <- c(-50, 75)
dtStart = as.Date('1980-01-01')
plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)

datay <- "GSPC.CloseBYMDY.Close"
ylim <- c(0, 20)
dtStart = as.Date('1980-01-01')
plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)

S&P 500 Normalized moving average

Look at moving average relationship by dividing the S&P 500 open price by the 200 day SMA.

datay <- "GSPC.Open_mva200_Norm"
ylim <- c(50, 125)
dt.start = as.Date('2008-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Crossovers

Look at the 50 DMA versus 200 DMA, often used as a technical indicator of market direction.

datay <- "GSPC.Open_mva050_mva200"
ylim <- c(-200, 200)
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStartBackTest)

datay <- "GSPC.Open_mva050_mva200_sig "
ylim <- c(0.0, 1.0)
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStartBackTest)

S&P 500 TTM P/E

Take a look at some of the earnings trends from SilverBlatt’s sheet.

## New names:
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## New names:
## * `` -> ...2
## * `` -> ...5
## * `` -> ...8
## New names:
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## * `` -> ...5
## * `` -> ...8
## New names:
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## * `` -> ...6
## * ...

Take a longer look back at as-reported and operating earnings

Market prices can out-run earnings so take a look at price to earnings.

Focus on some of the more recent activity

S&P 500 Sales

datay <- "MULTPLSP500SALESQUARTER"
ylim <- c(500, 1500)
dt.start <- as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

datay <- "MULTPLSP500SALESQUARTER"
ylim <- c(500, 1500)
dt.start = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Unit Profits

The series peaks in the middle of a bull market.

S&P 500 dividends

12-month real dividend per share inflation adjusted November, 2018 dollars. Data courtesy Standard & Poor’s and Robert Shiller.

https://www.quandl.com/data/MULTPL/SP500_DIV_MONTH-S-P-500-Dividend-by-Month

Evaluate year over year dividend growth.

Real value dividend growth.

datay <- "MULTPLSP500DIVMONTH_YoY"
ylim <- c(-40, 20)
dtStart = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

S&P 500 dividend yield (12 month dividend per share)/price. Yields following September 2018 (including the current yield) are estimated based on 12 month dividends through September 2018, as reported by S&P. Sources: Standard & Poor’s for current S&P 500 Dividend Yield. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Dividend Yields.

https://www.quandl.com/data/MULTPL/SP500_DIV_YIELD_MONTH-S-P-500-Dividend-Yield-by-Month

datay <- "MULTPLSP500DIVYIELDMONTH"
ylim <- c(0, 12)
dtStart = as.Date('1950-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

datay <- "MULTPLSP500DIVYIELDMONTH"
ylim <- c(1, 4)
dtStart = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

S&P 500 Volume

The log of the S&P volume has some interesting patterns, but nothing that seems to help with a recession indicator.

That is one spiky data series. Not sure there is a lot to help us here.

Russell 2000

Take a look at recent activity in the small cap market.

S&P 500 to Rusell 2000

Thirty day movement

Correlation

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S&P 500 to MDY (Mid-cap) 2000 Correlation

datay1 <- "RLG.Open"
ylim1 <- c(0, 2500)

datay2 <- "MDY.Open"
ylim2 <- c(0, 500)

dtStart <- as.Date("1jan2003","%d%b%Y")

w <- 30
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)
## Warning in max.default(structure(numeric(0), class = "Date"),
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## structure(numeric(0), class = "Date"), : no non-missing arguments to min;
## returning Inf

Dividend Stocks

This is an interesting series, they should perform better through the recessions. Unfortunately they are short lived so there is not much data so this is more of a place holder for now.

datay <- "NOBL.Open"
ylim <- c(40, 110)
dt.start <- as.Date('2014-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Margin and option data

NYSE Margin Debt

Taking a look at margin debt. NYXDATA stopped providing NYSE margin debt data on Dec 2017. Data is available from FINRA, but it includes more accounts than the data did for NYXdata. I stitched togeter the data sets: data after Jan 2010 include NYSE+Others, data prior is just NYSE account data scaled up to match the FINRA data.

It tends to creep up when there is a frenzy in the stock market.

datay <- "FINRAMarginDebt_Log"
ylim <- c(5, 15)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Take a close look at recent activity

Sometimes it is more helpful to view year over year growth.

More near-term trend.

Take a look at some of the correlations

datay1 <- "FINRAMarginDebt_YoY"
ylim1 <- c(-100, 100)

datay2 <- "GSPC.Close_YoY"
ylim2 <- c(-100, 100)

dtStart <- as.Date("1jan1995","%d%b%Y")

w <- 90
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

Comparison to the Russell 2000

datay1 <- "FINRAMarginDebt_YoY"
ylim1 <- c(-100, 100)

datay2 <- "RLG.Close_YoY"
ylim2 <- c(-100, 100)

dtStart <- as.Date("1jan1995","%d%b%Y")

w <- 90
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

OCC Options Volumes

See what is happening with the options volumes for equities. (From: https://www.theocc.com/webapps/historical-volume-query)

Looks like options on non-equity co-occurs with peaks/troughs?.

Market Volatility

Take a look at some of the indications of market volatility

CBOE VIX

As markets become complacent (low VIX) and high values, peaks often occur.

Compare the VIX to some of the ETF’s out there.

There

Not much predictive in VIX, take a quick look at the smoothed derivative.

S&P Daily Swings

Daily changes in the S&P should correlate well with the VIX.

More of a correlating series than a predictor.

Employment and payrolls

Unemployment rates

Unemployment rates will probably be useful, let’s take a look at the U-3. The data is a little noisy so there is also a smoothed version plotted. There seems to be a relationship between the unemployment rate and the recessions, but it could be a lagging indicator. This will be explored a little bit more later.

Looking at the unemployment rate, the eye is drawn to the rise and fall of the data, this suggests that the derivative might be helpful as well. The figure below shows the results, using a Savitzky-Golay FIR filter. It looks like the unemployment rate peaks in the middel of the recession. That peak might be a good buy signal.

Continuing Claims

A good measure of how much unemployment is growing.

Continued claims, also referred to as insured unemployment, is the number of people who have already filed an initial claim and who have experienced a week of unemployment and then filed a continued claim to claim benefits for that week of unemployment. Continued claims data are based on the week of unemployment, not the week when the initial claim was filed

https://fred.stlouisfed.org/series/CCNSA

A good measure of how much unemployment is growing

Unemployment rates, year-over-year

Both the headline unemployment and U-6 number changes are similar. During the upswing on the cycle it does look like the headline number falls faster than U-6

The second derivative of the unemployment rate does have zero crossings near the middle point of a recession. This would make it a helpful buy signal for the trading strategy.

Unemployment rates, similar periods

Historically the last two years of record low unemployment appear most similar to the 1971-1973 time frame. Just before inflation took off.

Unemployment rates, U-6 and headline number.

Let’s also take a look at the total unemployed, U-6. It continues to fall as the headline number stabilizes as people return to the work force. An indicator the cycle is beginning to top out.

Difference between U6 and U3 to see how close the economy is getting to full employment.

Unemployment and market bottoms

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Initial jobless claims

We will also take a look at initial jobless claims, this should start to rise just before the unemployment rate.

It looks like the jobless claim tend to peak more towards the end of the recession. It does not seem to be as strong of a sell indicator as the U-3 rate.

Jobless claims have a seasonal component to them. One way to reduce this effect is to calculate year over year growth. That helps some, the peaks seem to be more closely aligned with the middle to end of recessions.

Take a closer look at recent data

Take a look at the percentage of the population looking for work

A bit more recent trend

Unemployment Level

ADP data here. comes out before the official numbers.

Look at the year-over-year change in ADP.

ADP data divided by the population

Payrolls

Look at the BLS data on payrolls. Check the NSA series, then we will look at YoY data.

Hours worked

Sparked by an article at Mises (https://mises.org/wire/how-alexandria-ocasio-cortez-misunderstands-american-poverty), take a look at average weekly hours

The time series is pretty lumpy, plot the YoY change

A more recent look at average weekly hours of production

Industrial Production

Industrial production is also known to fall during an economic downturm, let’s take a look at some of the data from the FRED on industrual production. It does seem to peak prior to a recession so let’s smooth and look at the derivative as it might be a good indicator as well.

Industrial production over the last ten years or so

The derivative isn’t bad, but it sometimes crosses zeros well into a recession. That is less helpful as either a buy or sell indicator. A better measure might year over year (YoY) change.

The year over year change has a similar appearance. The low values at the beginning make the year over year values larger than the more recent values. Seems like it will rank low a reliable indicator.

datay1 <- "INDPRO_YoY"
ylim1 <- c(-20, 12)

datay2 <- "GSPC.Close_YoY"
ylim2 <- c(-100, 50)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Retail Sales

Retail sales, aggregate

Retail sales also change during recession. As the plot below shows, it seems to follow the trend of industrial production. It might be too strongly correlated to add much to the model. The will be examined in the correlation section.

The derivative of retail sales is a little more erratic than is was the industrial products. Looks like it might be helpful to include in the model as well.

Retail sales, aggregate year-over-year

Take a look at year-over-year changes

Retail sales and unemployment correlations

Let’s see how that looks on year over year basis. Interesting to compare to unemployment rates there appears to a correlation over the long term.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

There is some similarity. The rolling correlation shows the inverse relationship prior to a recession.

datay1 <- "RSALESAGG_YoY"
ylim1 <- c(-12.5, 7.5)

datay2 <- "UNEMPLOY_YoY"
ylim2 <- c(-30, 100)

dtStart <- as.Date("1jan1970","%d%b%Y")

w <- 180
corrName <- calcRollingCorr(dfRecession,df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Retail sales correlation and industrial production

Industrial production and retail sales look very similar so the plot below shows the 360 correlation. The corerlation does tend to fall around a recession, although 2008 was so bad that they both fell together. Not sure if it is that useful.

datay1 <- "INDPRO"
ylim1 <- c(40, 125)

datay2 <- "RSALESAGG"
ylim2 <- c(100000, 200000)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

It is interesting to see the strong correlation; however, I suspect this is due to more to the shape of the trends. How do the YoY correlations look? They are a little less correlated, probably better to use in the machine learning later.

datay1 <- "INDPRO_YoY"
ylim1 <- c(-20, 20)

datay2 <- "RSALESAGG_YoY"
ylim2 <- c(-20, 20)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Advance Retail Sales

This is an advanced estimate of the retail sales value.

Also take a look at year over year

Retail sales and the labor market

Income

Real Personal Income

Real Personal Income (Excluding Transfer, Annual)

During a recession real personal income falls. In the plot the peaks can be seen prior to each recession.

datay <- "W875RX1"
ylim <- c(3000, 15000)
plotSingleQuickModern(datay, ylim)

The features we are interested in are the peaks and valleys so we’ll use the derivative to get to those. Interesting, there is usually a first zero crossing before a recession and a second during or just after the recession.

Real personal income might have some seasonal variance, but it seems the year over year change tells the same story.

Price and cost measures

This section shows price and cost measures.

Two commonly used indexes are the CPI (consumer price index) and PPI (producer price index). CPI tries to show final prices paid for goods and services by urban U.S. consumers. This index includes sales tax and imports. The PPI attempts to reflect the prices paid at all stages of production, including goods and services purchases as inputs as well as goods and services purchased by consumers from retail and producer sellers. The PPI does not include imports or sales tax. The CPI reflects all rebates and financing plans wherease the PPI reflects only those rebate and financing plans provided by the producer. For example if an automotive manufacturer offers a rebate of $500 and the dealer offers an additional rebate of $500 then the PPI would reflect only the automotive manufacturer rebate, but the CPI would reflect both rebates.

Sources; https://www.bls.gov/opub/hom/pdf/cpihom.pdf and https://www.bls.gov/opub/hom/pdf/ppi-20111028.pdf.

Consumer price index

What does CPI look like?

datay <- "CPIAUCSL"
ylim <- c(0, 300)
plotSingleQuickModern(datay, ylim)

Check out the YoY growth

datay <- "CPIAUCSL_YoY"
ylim <- c(-2, 15)
plotSingleQuickModern(datay, ylim)

CPI to PPI

Suggested by Charlie, it can be helpful to look at the relationship between producer prices and consumer prices.

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## replace the existing scale.

Producer Price Index (Commodities)

Commodities

Basket

Take a look at some trends of baskets of commodities.

Crude oil

Look at a trend of West Texas Intermediate (WTI)

This is ticker data from yahoo

Take a look at both WTI and Brent crude.

Real price of crude using producer price index for commodities

Gold

As risks increase investors often flock to safe haven assets like gold. An up-tick in prices can indicate investor uncertainty. This can be seen in the nominal price plot around 1980 and again in 2007.

This plots out the real price of gold by two different deflators. PPI corrected price is a little higher, to be expected since CPI also includes the effects of sales tax and imports. The spike in 1980 is especially pronounced in this series.

See how nominal and real prices look year over year. From the long-term view seems like there is little difference in the three series. Although not shown, even over the near-term there is little difference in the series.

See how gold correlates with the VIX. Both gold and VIX should respond to investor axiety, but it doesn’t look like it correlates very well.

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 245 rows containing non-finite values (stat_smooth).

Copper

Dr. Copper has a reputation as an indicator of economic malaise, but it does not seem to have much of a correlation with the recessions. The series below is from CME via Quandl. It has a lot of data so I am also looking at the smoothed version.

Copper is one of the commodities in the PPI so it is a bit of a proxy for how copper is doing relative to the basket of commodities.

The change in prices, year over year, do generally peak prior to a recession. The time and shape of this peak varies, but it still might be helpful. A couple of the large troughs do seem to correlate with the end of the recession. Likely this is because industrial production has also fallen.

There is some correlation between copper and the smooth recession initiator, especially at the end of the recession.

Might be easier to see correlation in a dot plot format.

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 342 rows containing non-finite values (stat_smooth).

This is a legacy series from FRED. It has not been updated in a couple of years so I am assuming it will go away.

Oil Services

Amazing events in the first half of 2020, take a look at those

See how the players are doing

Federal Reserve

The federal reserve has an impact on the economy, here are some data series relating to that.

Little bit closer

datay <- "WALCL"
ylim <- c(0, 8500)
dtStart = as.Date('2003-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Federal Reserve Reverse Repo Agreements

Compare liabilities to reverse repo trends

Spiky, might be easier to look at year-over-year

Normalized by GDP

datay <- "WLRRAL.by.GDP"
ylim <- c(0, 4)
dtStart = as.Date('2003-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Overnight Bank Funding Rate

“The overnight bank funding rate is calculated using federal funds transactions and certain Eurodollar transactions. The federal funds market consists of domestic unsecured borrowings in U.S. dollars by depository institutions from other depository institutions and certain other entities, primarily government-sponsored enterprises, while the Eurodollar market consists of unsecured U.S. dollar deposits held at banks or bank branches outside of the United States. U.S.-based banks can also take Eurodollar deposits domestically through international banking facilities (IBFs). The overnight bank funding rate (OBFR) is calculated as a volume-weighted median of overnight federal funds transactions and Eurodollar transactions reported in the FR 2420 Report of Selected Money Market Rates. Volume-weighted median is the rate associated with transactions at the 50th percentile of transaction volume. Specifically, the volume-weighted median rate is calculated by ordering the transactions from lowest to highest rate, taking the cumulative sum of volumes of these transactions, and identifying the rate associated with the trades at the 50th percentile of dollar volume. The published rates are the volume-weighted median transacted rate, rounded to the nearest basis point.” https://www.newyorkfed.org/markets/obfrinfo.

Secured Overnight Financing Rate

“The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of borrowing cash overnight collateralized by Treasury securities. The SOFR includes all trades in the Broad General Collateral Rate plus bilateral Treasury repurchase agreement (repo) transactions cleared through the Delivery-versus-Payment (DVP) service offered by the Fixed Income Clearing Corporation (FICC), which is filtered to remove a portion of transactions considered “specials” " https://apps.newyorkfed.org/markets/autorates/sofr

Take a look at the variation (99th - 1st percentile)

Reserve Balances with Federal Reserve Banks

Hard to get a sense of these series in the absolute. Take a look relative to GDP.

By double entry book-keeping reserves+loans (assets) = deposit (liabilities). Does that really work?

Correlation Between Reserves and Total Loans

As reserves increase there should be less lending. That correlation generally holds.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Did the reserve balances increase after the 2016 and 2018 drops? Not in the same way. There are some relationships between the equities market and the reserves though.

Explicitly correlate reserve balances and total loans. It is a weak and noisy correlation.

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 990 rows containing non-finite values (stat_smooth).

Interest on excess reserves

Monetary Base

Currency trend, base

This used to trend along with GDP. It doesn’t anymore.

Money supplies

Basic currency trend (currency component of M1)

datay <- "WCURRNS_YoY"
dtStart = as.Date('1980-01-01')
ylim <- c(0, 17)
myplot <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)
myplot

datay <- "WCURRNS_YoY"
dtStart = as.Date('2000-01-01')
ylim <- c(0, 20)
myplot <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)
myplot

The rate of change of money supply could be an indicator of a recession. Let’s see how that compares.

Intervention in the repo market

The federal reserve provides liquidity to the repo market, summary of that action

European central bank

The European central band (ECB) has taken a different path compared to the US Federal Reserve bank.

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Federal Debt

The government is a big driver of the economy, let’s see what it is doing in the debt markets.

datay <- "GFDEBTN"
ylim <- c(0, 28000000)
plotSingleQuick(dfRecession, df.data, datay, ylim)

datay <- "GFDEBTN_Log"
ylim <- c(12, 18)
plotSingleQuick(dfRecession, df.data, datay, ylim)

datay <- "GFDEBTN_YoY"
ylim <- c(-10, 25)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Federal debt as percent GDP

datay <- "GFDEGDQ188S"
ylim <- c(30, 150)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Federal deficit as percent GDP

datay <- "FYFSGDA188S"
ylim <- c(-30, 5)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Charlie Hatch has a nice format of deficit versus debt:

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## replace the existing scale.

Nonfinancial Corporate Business Debt

What about Nonfinancial corporate business and debt securities? Hopefully this doesn’t follow the business loan trends.

That is crazy steep. Time for a log format, see if that brings out the peaks and troughs. That’s a litte better, it looks like there might be a change in slope prior to the recessions.

The derivative doesn’t seem to be much help. There is not much correlation between the zero crossings and the NEBR recessions.

Debt cycle

This analysis roughly follows the ideas in Big Debt Crises book by Ray Dalio.

Total loans

One business cycle theory describes recessions as a market adjustment to mis-allocated assets, often fueled by an credit expansion. That makes the volume of loans an interesting feature to look at. In the presentation of data it looks like the great recession had the largest impact.

Plotting the year over year growth rate helps pull out those small changes in the early years in the data. Peaks can be seen prior to most recessions.

Zoom in to the last couple of decades

As long term interest rates rise, loans should start to tick down. To check this, the total loans and 10 to 1 year spreads are plotted. This is generally the trend observed.

There is a good correlation between these two variables. This next section plots that correction explicitly.

Total loans as percent of GDP

This is the total loans. I think the picture is too broad to point to a specific sector of the economy. The debt burden assumes interest rates are tied to the 10-year treasury: (TOTLNNSA * DGS10) / 100

Commercial and industral loans

Business loans should slow before the recession (a contraction in credit as rates rise).

Commercial and industrial loans as percent of GDP and and income

Look at business debt normalized by GDP over the entire time series. This ratio often peaks at the mid-point of a recession.

https://www.wsj.com/articles/this-isnt-your-fathers-corporate-bond-market-11590574555

“Bonds are behaving more like bank debt, which tends to remain stable or even increase at the onset of recessions, as lenders keep distressed clients afloat—and only later turn off the taps. This was confirmed by a recent report from the Bank for International Settlements. It also found a tight link between this lending cycle and the “real” economy’s booms and busts."

I assume that interest is related to the 10-year treasure: (TOTCINSA * DGS10) / 100

Farm loans

See how the farming sector is fairing.

Real estate loans

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

This gives a big picture, but makes it hard to connect the loans with the income needed to cover those loans. In the next section, loans will be broken up by commercial and residential.

Real Estate (Residential)

In absolute terms the mortgages have increased, but it does not appear to be out of line with the overall economy.

Normalized by GDP it is easier to see the peak in 2008 and that loan levels appear reasonable at the commercial banks.

Maybe the GSE’s are making loans. Take a look at the total mortgages from Z.1 as a percentage of GDP. That does not look too far off trend (ignoring that peak in 2008).

I am assuming that personal income is paying for the mortgages.

Real estate (residential) as percent of GDP and and income

## Warning: Removed 1 rows containing missing values (geom_text).

Consumer loans

Focusing on the consumer sector the growth in debt and incomes can be directly compared. Personal income, as a percent of GDP, remains nearly constant. It is not uncommon for the personal income to rise prior to a recession. Likely this reflect increasing asset prices and market returns. Also interesting to see the loans pick up after interest rates dropped in 1982.

Consumer loans as percent of GDP and and income

Take a closer look since the 2008 recession. Looks like loans are starting to slow as the interest burden rises and incomes remain stable. There are some anomolies in the A065RC1A027NBEA data series because it only updates onces a year. the PI series updates once a month but is noisier and seasonally adjusted. It also shows incomes rising in the middle of the 2008 recession, which doesn’t seem to be accurate.

## Warning: Removed 1 rows containing missing values (geom_text).

## Warning: Removed 1 rows containing missing values (geom_text).

Repo market

This market went through some stress in 2008, it is happening again so setup some plots to watch it.

Nonfincial corporate business security repo asset level

Bonds

T-Bills and Yield Curve

Speaking of loans, interest rates also play into this. This analysis will focus on treasure bills. The 3-month is plotted below. The yield flattens before a recession as investors go long on bonds and short on equities.

datay <- "TB3MS"
datay.aux <- "DTB3"
ylim <- c(0, 20)
p1 <- plotSingleQuickModern(datay, ylim)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

datay <- "TB3MS"
datay.aux <- "DTB3"
ylim <- c(0, 2.5)
dtStart = as.Date('2017-01-01')
p1 <- plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

Check out LIBOR and fed funds rate

The 1-year is plotted below. The yield flattens before a recession as investors go long on bonds and short on equities.

datay <- "DGS10"
datay.aux <- "TNX.Close"
ylim <- c(0, 20)
p1 <- plotSingleQuickModern(datay, ylim)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

Close in, the trend towards inversion be more easily seen. I am also comparing data from the CBOE as well as FRED.

Bond yields are a good proxy for interest rates. As rates rise the theory goes that loans should decrease (inverse correlation).

And a longer window

The yield curve (30 year bond rate minus the 10 year bond rate) may not be a good recession indicator, but a collapse is not good (https://blogs.wsj.com/moneybeat/2018/04/30/theres-more-than-one-part-of-the-yield-curve-getting-flatter/).

The yield curve (10 year bond rate minus the 1 year bond rate) seems to a good indicator of an oncoming recession. It could be a buy indicator by itself.

More recent data

Just the last 24 months or so.

Plot the 10 Year to 3 month over a few decades to see what the outling cases look like

The last two year compare favorably with the period around the 2015-2016 turndown, driven primarily by slowing of the Chinese GDP. Not a debt-driven cycle.

This plot format was suggested by a mises.org article (https://mises.org/wire/yield-curve-accordion-theory), but they only went back to 1988. The date seemed arbitrary so I went back further in time.

Take a look at more recent data

Try looking at a 1-year average of the above time series

High quality bonds

datay <- "AAA"
ylim <- c(2.5, 10)
dtStart = as.Date('1997-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High quality bonds to 10-year treasury

High quality bonds long-term trend.

datay <- "DGS10ByAAA"
ylim <- c(1, 6.0)
dtStart = as.Date('1967-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High quality bonds near-term trend.

datay <- "DGS10ByAAA"
ylim <- c(1, 6.0)
dtStart = as.Date('2007-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High yield spread

“This data represents the Option-Adjusted Spread (OAS) of the ICE BofAML US Corporate A Index, a subset of the ICE BofAML US Corporate Master Index tracking the performance of US dollar denominated investment grade rated corporate debt publicly issued in the US domestic market. This subset includes all securities with a given investment grade rating A. The ICE BofAML OASs are the calculated spreads between a computed OAS index of all bonds in a given rating category and a spot Treasury curve. An OAS index is constructed using each constituent bond‚Äôs OAS, weighted by market capitalization. When the last calendar day of the month takes place on the weekend, weekend observations will occur as a result of month ending accrued interest adjustments.”

  • ICE Benchmark Administration Limited (IBA), ICE BofAML US Corporate A Option-Adjusted Spread [BAMLC0A3CA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BAMLC0A3CA, July 4, 2019.
datay <- "BAMLC0A3CA"
ylim <- c(0, 7)
dtStart = as.Date('1997-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Municipal bond market

Suggest by a WSJ article, change in volume for high-risk muni’s. Doesn’t look like there is much too it yet.

https://www.wsj.com/articles/risky-municipal-bonds-are-on-a-hot-streak-11558949401?mod=hp_lead_pos3

datay <- "HYMB.Close"
ylim <- c(40, 62)
dtStart = as.Date('2011-01-01')
p1 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

datay <- "HYMB.Volume"
ylim <- c(0, 1750000)
p1.vol <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )


datay <- "GSPC.Open"
datay_aux <- "GSPC.Close"
ylim <- c(1500, d.GSPC.max )
p2 <-
  plotSingle(
    dfRecession,
    df.data,
    "date",
    datay,
    getPlotTitle(df.symbols, datay),
    "Date",
    getPlotYLabel(df.symbols, datay),
    c(dtStart, Sys.Date()),
    ylim,
    TRUE
  )

p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )


grid.arrange(p1,
             p1.vol,
             p2,
             ncol = 1,
             top = "High Yield Muni's and S&P Price")

Total Loans and yield curve correlation

This relationship was suggest by Charlie and it is an interesting one. As the yield curve flattens (10-year and 1-year rates converge), total loans grow. The generalization is not always accurate, but it does fit.

## `geom_smooth()` using formula 'y ~ x'

I wanted to see how this looked compared to the 3 month

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 282 rows containing non-finite values (stat_smooth).

Consumer loans and yield curve correlation

Compared to business loans, consumer loans seem to have to response to the 10Y to 3M yield curve.

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 311 rows containing non-finite values (stat_smooth).

Business loans and yield curve correlation

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 103 rows containing non-finite values (stat_smooth).

That’s pretty good correlation. Let’s see what the rolling correlation looks like.

datay1 <- "TOTLNNSA_YoY"
ylim1 <- c(-10, 20)

datay2 <- "DGS10TO1"
ylim2 <- c(-5, 10)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

datay1 <- "TOTLNNSA_YoY"
ylim1 <- c(-10, 20)

datay2 <- "DGS10TO1"
ylim2 <- c(-5, 10)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 720
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

One other items, let’s see how loans do versus the federal funds rate

## `geom_smooth()` using formula 'y ~ x'

EIA Information

Petroleum

Consumption/Sales > Refiner Motor Gasoline Sales Volumes > by Product > Motor Gasoline > by Area > U.S.

Total Energy

Crude Oil and Natural Gas Resource Development > Crude Oil and Natural Gas Drilling Activity Measurements

Baker Hughes Rig Count

BEA Supplemental Estimates, Motor Vehicles

Definitions

Autos–all passenger cars, including station wagons.
Light trucks–trucks up to 14,000 pounds gross vehicle weight, including minivans and
sport utility vehicles. Prior to the 2003 Benchmark Revision light trucks were up to 10,000 pounds.
Heavy trucks–trucks more than 14,000 pounds gross vehicle weight.
Prior to the 2003 Benchmark Revision heavy trucks were more than 10,000 pounds.
Domestic sales–United States (U.S.) sales of vehicles assembled in the U.S., Canada, and Mexico.
Foreign sales–U.S. sales of vehicles produced elsewhere.
Domestic auto production–Autos assembled in the U.S.
Domestic auto inventories–U.S. inventories of vehicles assembled in the U.S., Canada, and Mexico.

TAble 6 - Light Vehicle and Total Vehicle Sales

Auto sales

A WSJ article suggested that auto sales might be a good indicator so bring that to the mix. It does have troughs that correlate with recessions

There might be some seasonal variance in the auto sales so lets take a look at the year over year. The data is pretty noisy, it probably will not make a very good indicator.

BEA Gross Domestic Product

Data in this section come from the Bureau of Economic Analysis.

Table 1.1.5. Gross Domestic Product

[Billions of dollars] Seasonally adjusted at annual rates

A191RC: Gross Domestic Product - Line 1

GDP numbers tend to lag so this series is truly an afterthought. But it does have some correlation with the recessions.

GDP does not reflect the capacity of the economy nor the efficiency. Shrinking capacity and lower prices at constant volumes would indicate improvements in effeciency/productivity which is good for the economy, but does not move the GDP upward.

Looks like the year over year change on the GDP should correlate well with unemployment.

Table 1.1.9. Implicit Price Deflators for Gross Domestic Product

[Index numbers, 2012=100] Seasonally adjusted

A191RD: Gross Domestic Product - Line 1

This is GDP price deflator series.

GDP normalized by CPI

Normalize GDP by CPI

Economic yield curve (GDP to 1-year treasury)

GDP versus the yield on the 1-year. This series was prompted by an article suggesting that the “economic yield curve” should be used to indicate a recession rather than an inverted yield curve. Less of indicator and more of concurrent confirmation of recession. Not sure why they would be related either.

Economic yield curve (GDP to 3-month treasury)

Same idea as above, but applied the 3-month treasury.This one has fewer false triggers, but is not as helpful as 10Y to 3M spread in predicting a recession.

A824RC: National defense Federal Gov’t Expenditures - Line 24

U.S. Bureau of Economic Analysis, Federal Government: National Defense Consumption Expenditures and Gross Investment [FDEFX], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FDEFX, April 6, 2021.

A825RC: Nondefense Federal Gov’t Expenditures - Line 25

U.S. Bureau of Economic Analysis, Federal Government: Nondefense Consumption Expenditures and Gross Investment [FNDEFX], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FNDEFX, April 6, 2021.

Table 6.16D. Corporate Profits by Industry

Select series from Table 6.16D

A051RC: Corporate profits with inventory and capital consumption adjustment

From BEA’s documentation (https://www.bea.gov/media/5671):

“BEA’s featured measure of corporate profits — profits from current production - provides a comprehensive and consistent economic measure of the income earned by all U.S. corporations. As such, it is unaffected by changes in tax laws, and it is adjusted for nonreported and misreported income. It excludes dividend income, capital gains and losses, and other financial flows and adjustments, such as deduction for “bad debt.” Thus, the NIPA measure of profits is a particularly useful analytical measure of the health of the corporate sector. For example, in contrast to other popular measures of corporate profits, the NIPA measure did not show the large run-up in profits during the late 1990s that was primarily attributable to capital gains.

Profits after tax with IVA and CCAdj is equal to corporate profits with IVA and CCAdj less taxes on corporate income. It provides an after-tax measure of profits from current production."

Data is Line 1 of Table 6.16D

A053RC: Corporate profits without inventory and capital consumption adjustment

Profits look a bit flat over the last several years in this series.

Table 2.6. Personal Income and Its Disposition, Monthly

Billions of dollars; months are seasonally adjusted at annual rates.

A065RC Personal Income - Line 1

BEA Account Code: A065RC

Personal income is the income that persons receive in return for their provision of labor, land, and capital used in current production and the net current transfer payments that they receive from business and from government.25 Personal income is equal to national income minus corporate profits with inventory valuation and capital consumption adjustments, taxes on production and imports less subsidies, contributions for government social insurance, net interest and miscellaneous payments on assets, business current transfer payments (net), current surplus of government enterprises, and wage accruals less disbursements, plus personal income receipts on assets and personal current transfer receipts. A Guide to the National Income and Product Accounts of the United States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Income [PI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PI, July 11, 2019.

DPCERC: Personal consumption expenditures (PCE) - Table 2.1, Line 29

BEA Account Code: DPCERC Personal consumption expenditures (PCE) is the primary measure of consumer spending on goods and services in the U.S. economy. 1 It accounts for about two-thirds of domestic final spending, and thus it is the primary engine that drives future economic growth. PCE shows how much of the income earned by households is being spent on current consumption as opposed to how much is being saved for future consumption. -https://www.bea.gov/system/files/2019-12/Chapter-5.pdf

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Consumption Expenditures [PCE], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCE, June 12, 2020

DPCERG: Personal consumption expenditures Price Index (PCEPI) - Table 2.1, Line 29

BEA Account Code: DPCERG The gross domestic product price index measures changes in prices paid for goods and services produced in the United States, including those exported to other countries. Prices of imports are excluded. The gross domestic product implicit price deflator, or GDP deflator, basically measures the same things and closely mirrors the GDP price index, although the two price measures are calculated differently. The GDP deflator is used by some firms to adjust payments in contracts.

The gross domestic purchases price index is BEA’s featured measure of inflation for the U.S. economy overall. It measures changes in prices paid by consumers, businesses, and governments in the United States, including the prices of the imports they buy.

BEA’s closely followed personal consumption expenditures price index, or PCE price index, is a narrower measure. It looks at the changing prices of goods and services purchased by consumers in the United States. It’s similar to the Bureau of Labor Statistics’ consumer price index for urban consumers. The two indexes, which have their own purposes and uses, are constructed differently, resulting in different inflation rates.

The PCE price index is known for capturing inflation (or deflation) across a wide range of consumer expenses and for reflecting changes in consumer behavior. For example, if the price of beef rises, shoppers may buy less beef and more chicken. Also, BEA revises previously published PCE data to reflect updated information or new methodology, providing consistency across decades of data that’s valuable for researchers. The PCE price index is used primarily for macroeconomic analysis and forecasting. -https://www.bea.gov/resources/learning-center/what-to-know-prices-inflation

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Consumption Expenditures: Chain-type Price Index [PCEPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCEPI, April 25, 2021.

A072RC: Personal Savings Rate - Line 35

Consumers tend to pull down their savings rates as unemployment decreases and market conditions improve. This series has tended to be unreliable due to the size of revisions during the comprehensive update carried out by the BEA. The last update on this series moved the rate from 4.2 to 6.7 percent.

(https://www.bloomberg.com/news/articles/2018-07-27/americans-have-been-saving-much-more-than-thought-new-data-show)

BEA Account Code: A072RC Personal saving as a percentage of disposable personal income (DPI), frequently referred to as “the personal saving rate,” is calculated as the ratio of personal saving to DPI. Personal saving is equal to personal income less personal outlays and personal taxes; it may generally be viewed as the portion of personal income that is used either to provide funds to capital markets or to invest in real assets such as residences.(https://www.bea.gov/national/pdf/all-chapters.pdf) A Guide to the National Income and Product Accounts of the United States (NIPA).

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Saving Rate [PSAVERT], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PSAVERT, July 9, 2019.

Take a closer look at the last decade

The relationship between personal savings and unemployment (U-3) can be better visualized with a scatter plot

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 189 rows containing non-finite values (stat_smooth).

The fit does not explain most of what is in the plot. Lets take a look at the rolling correlation.

datay1 <- "UNRATE"
ylim1 <- c(2, 12)

datay2 <- "PSAVERT"
ylim2 <- c(0, 35)

dtStart <- as.Date("1jan1985","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Personal savings to household net worth

A relationship between personal savings and household networth can be seen in a scatter plot. This was suggested by a WSJ article (https://blogs.wsj.com/dailyshot/2018/02/23/the-daily-shot-reasons-for-declining-u-s-household-savings-rate/).

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 216 rows containing non-finite values (stat_smooth).

U.S. Census Bureau

U.S. International Trade in Goods and Services (FT900)

U.S. Bureau of Economic Analysis and U.S. Census Bureau, U.S. Imports of Goods by Customs Basis from China [IMPCH], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/IMPCH, October 5, 2019.

New Houses Sold and For Sale by Stage of Construction and Median Number of Months on Sales Market

Read an article suggesting that housing sales and sales growth could be useful. FRED only has new home data so start there.

datay <- "HSN1FNSA"
ylim <- c(0, 200)
dtStart = as.Date('1964-01-01')
p1 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "HNFSUSNSA"
ylim <- c(0, 600)
p2 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "HNFSUSNSA.minus.HSN1FNSA"
ylim <- c(0, 600)
p3 <-
  plotSingle(
    dfRecession,
    df.data,
    "date",
    datay,
    getPlotTitle(df.symbols, datay),
    "Date",
    getPlotYLabel(df.symbols, datay),
    c(dtStart, Sys.Date()),
    ylim,
    TRUE
  )

grid.arrange(p1,
             p2,
             p3,
             ncol = 1,
             top = "New Housing Sales")

New housing yoy

New Privately-Owned Housing Units Authorized in Permit-Issuing Places

As provided by the Census, start occurs when excavation begins for the footings or foundation of a building. All housing units in a multifamily building are defined as being started when this excavation begins. Beginning with data for September 1992, estimates of housing starts include units in structures being totally rebuilt on an existing foundation.

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, Housing Starts: Total: New Privately Owned Housing Units Started [HOUST], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/HOUST, June 13, 2020.

Take a look at privately owned starts

New Privately-Owned Houses Sold and For Sale

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, Median Sales Price of Houses Sold for the United States [MSPUS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/MSPUS, June 13, 2020.

Finally, take a look at starts times the median price

Durable Goods

Suggested Citation: U.S. Census Bureau, Manufacturers’ New Orders: Durable Goods [UMDMNO], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/UMDMNO, April 26, 2021.

Durable goods, not seasonally adjusted, divided by GDP

Durable goods, seasonally adjusted, divided by GDP

Federal reserve board H.8: Assets and Liabilities of Commercial Banks in the United States

Page 4: Not Seasonally adjusted, billions of dollars

Commercial and industrial loans, all commercial banks - Line 10

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

Suggested Citation: Board of Governors of the Federal Reserve System (US), Commercial and Industrial Loans, All Commercial Banks [BUSLOANS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BUSLOANS, July 11, 2019.

Taking a look at the difference in SA and NSA series. Seasonal adjustments do vary, but do not seem to be related to recessions.

The raw series is just too steep for any kind of machine learnine. This needs to be converted to log scale.

That’s a little better, let’s see what the smoothed derivative looks like.

That is odd…looks like this doesn’t cross zero unless we are getting close to, or into, a recession. The year over year tells about the same story. Might be a good indication of the end of a recession.

Consumer loans, all commercial banks - Line 20

Suggested Citation: Board of Governors of the Federal Reserve System (US), Consumer Loans, All Commercial Banks [CONSUMERNSA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CONSUMERNSA, July 11, 2019.

That spike in consumer loans is due to

“April 9, 2010 (Last revised September 23, 2011): As of the week ending March 31, 2010, domestically chartered banks and foreign-related institutions had consolidated onto their balance sheets the following assets and liabilities of off-balance-sheet vehicles, owing to the adoption of FASB’s Financial Accounting Statements No. 166 (FAS 166),”Accounting for Transfers of Financial Assets," and No. 167 (FAS 167), “Amendments to FASB Interpretation No. 46(R).”

This included a consumer loans, credit cards and other revolving plans change of $321.9B. That was a lot of off-balance-sheet bank assets.

Deposits, All Commercial Banks, all commercial banks - Line 34

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

Suggested Citation: Board of Governors of the Federal Reserve System (US), Deposits, All Commercial Banks [DPSACBW027SBOG], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/DPSACBW027SBOG, May 14, 2020.

Federal reserve board Z.1: Financial Accounts of the United States

From the FRED website (https://fred.stlouisfed.org/release?rid=52):

"The Financial Accounts (formerly known as the Flow of Funds accounts) are a set of financial accounts used to track the sources and uses of funds by sector. They are a component of a system of macroeconomic accounts including the National Income and Product accounts (NIPA) and balance of payments accounts, all of which serve as a comprehensive set of information on the economy’s performance.(1) Some important inferences that can be drawn from the Financial accounts are the financial strength of a given sector, new economic trends, changes in the composition of wealth, and development of new financial instruments over time.(1)

Sectors are compiled into three categories: households, nonfinancial businesses, and banks. The sources of funds for a sector are its internal funds (savings from income after consumption) and external funds (loans from banks and other financial intermediaries). (1) Funds for a given sector are used for its investments in physical and financial assets. Dividing sources and uses of funds into two categories helps the staff of the Federal Reserve System pay particular attention to external sources of funds and financial uses of funds.(2) One example is whether households are borrowing more from banks—or in other words, whether household debt is rising. Another example might be whether banks are using more of their funds to provide loans to consumers. Transactions within a sector are not shown in the accounts; however, transactions between sectors are.(2) Monitoring the external flows of funds provides insights into a sector’s health and the performance of the economy as a whole.

Data for the Financial accounts are compiled from a large number of reports and publications, including regulatory reports such as those submitted by banks, tax filings, and surveys conducted by the Federal Reserve System.(2) The Financial accounts are published quarterly as a set of tables in the Federal Reserve’s Z.1 statistical release.

  1. Teplin, Albert M. “The U.S. Flow of Funds Accounts and Their Uses.” Federal Reserve Bulletin, July 2001; http://www.federalreserve.gov/pubs/bulletin/2001/0701lead.pdf.
  2. Board of Governors of the Federal Reserve System. “Guide to the Flow of Funds Accounts.” 2000, http://www.federalreserve.gov/apps/fof/."

L.102 Nonfinancial Business

FL102051003.Q: Nonfinancial corporate business; security repurchase agreements; asset

Asset level of nonfinancial business security repo agreements. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL102051003&t=

L.214 Loans

FL894123005.Q: All sectors; total loans; liability

Sum of domestic financial sectors, all sectors, total mortgages, and households/non-profits. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL894123005&t=L.107&bc=L.107:FL793068005&suf=Q

FL793068005.Q: Domestic financial sectors; depository institution loans n.e.c.; asset

Sum of Monetary authority; depository institution loans n.e.c.; asset and Private depository institutions; depository institution loans n.e.c.; asset. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL793068005&t=L.214&suf=Q

FL893169005.Q: All sectors; other loans and advances; liability

Sum of finance, government, and chartered institutions asset levels. https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893169005&t=L.214&suf=Q

FL893065105.Q: All sectors; home mortgages; asset

https://www.federalreserve.gov/apps/fof/DisplayTable.aspx?t=L.214

FL893065405.Q: All sectors; multifamily residential mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065405&t=L.214&suf=Q

FL893065505.Q: All sectors; commercial mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065505&t=L.214&suf=Q

FL153166000.Q: Households and nonprofit organizations; consumer credit; liability

federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL153166000&t=L.214&suf=Q

B.101 Balance Sheet of Households and Nonprofit Organizations

FL152000005.Q: Households and nonprofit organizations; total assets, Level

string.source ID: FL152000005.Q.

FL152090006.Q: Household Net Worth as Percentage of Disposable Personal Income

string.source ID: FL152090006.Q. Household networth tends to fall as a recession start.

Productivity Yield Curve

GDP versus productivity

Manufacturing output and employees

Not sure if these relates to a recession, but fascinating to see how output and employees change with time.

datay <- "OUTMS"
ylim <- c(60, 120)
dtStart = as.Date('1987-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "MANEMP"
ylim <- c(10000, 20000)
dtStart = as.Date('1948-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "PRS30006163"
ylim <- c(40, 120)
dtStart = as.Date('1986-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Shipping volumes might be helpful in determining state of the economy.

datay <- "FRGSHPUSM649NCIS"
ylim <- c(0.8, 1.4)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "FRGSHPUSM649NCIS_YoY"
ylim <- c(-30, 30)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Freight, loosely, moves inversely to the trade deficit.

datay <- "BOPGTB_YoY"
ylim <- c(-30, 30)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

World bank air transportation. Only updated annually so less usefull, but interesting reference to above.

datay <- "WWDIWLDISAIRGOODMTK1"
ylim <- c(0, 250000)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Gross private domestic investment

Spending most certainly tips down prior to a recession. The gross private domestic investment data series, plotted in log format below, show how private investment pulls back prior to recessions.

The change in direction is a little easier to see if the derivative is plotted, first YoY then the smoothed derivative

Velocity

Productivity

Date range to match census data

PMI

Industrial Production

This is a look at manufacturing industrial production. The yoY change should be a leading indicator of unemployment.

Housing

Take a look at housing starts. These can drop as rates rise.

Case-schiller price index

Population data

Many of the economic series can be better understood if normalized by population. Basic population and worker data from FRED.

Population to GDP

Look at GDP divided by CPI per person. It flattens and even dips a little prior to a recession. Might be worth looking at the derivative of this series.

That is worth a closer look

datay1 <- "GDPBYCPIAUCSLBYPOPTHM_SmoothDer"
ylim1 <- c(-5, 5)

datay2 <- "RecInit_Smooth"
ylim2 <- c(0, 1)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Correlation Study

Detailed correlations are explored above. Before concluding, let’s take a look at some overall correlation values to see if anything pops out.

Commodities

As mentioned above, copper, year over year, has some correlation with the recession initiation. It could be useful.

GDP Series

GDP, normalized first by CPI and then by population, looks like it migh correlate inversely with the recession indicators

Financials

Let’s see where we are so far. The correlation plot confirms some of the speculation above. The S&P 500 (GSPC.Open) is well correlated with industrial production (INDPRO), business loans (BUSLOANS), total loans (TOTLNNSA) , and nonfinancial corporate business debt (NCBDBIQ027S).

In this case, I want and indicator that rises prior to a recession. It looks like the unemployment rate (UNRATE), real personal income (W875RX1), and the yield curve (DGS10TO1) are all inversely correlated with the recession initiation indicator.

I thought the modified recession initiation would be a harder match, but there are quite a few correlated variables. Lets take a look at some of those in more detail

Complete list of symbols

Since it is tedious to do this one at a time, all the symbols were entered into a data frame, loaded, and aggregated together in a single xts object.

This is the complete list of symbol names and sources used in the project.